diff --git a/README.md b/README.md
index a91e4352..ac7eaefd 100644
--- a/README.md
+++ b/README.md
@@ -255,6 +255,15 @@ In the GIS world, rasters are used for representing continuous phenomena (e.g. e
 
 --------
 
+### **Reproject / Merge**
+
+| Name | Description | Source | NumPy xr.DataArray | Dask xr.DataArray | CuPy GPU xr.DataArray | Dask GPU xr.DataArray |
+|:----------:|:------------|:------:|:----------------------:|:--------------------:|:-------------------:|:------:|
+| [Reproject](xrspatial/reproject/__init__.py) | Reprojects a raster to a new CRS using an approximate transform and numba JIT resampling | Standard (inverse mapping) | ✅️ | ✅️ | ✅️ | ✅️ |
+| [Merge](xrspatial/reproject/__init__.py) | Merges multiple rasters into a single mosaic with configurable overlap strategy | Standard (mosaic) | ✅️ | ✅️ | 🔄 | 🔄 |
+
+-------
+
 ### **Raster / Vector Conversion**
 
 | Name | Description | Source | NumPy xr.DataArray | Dask xr.DataArray | CuPy GPU xr.DataArray | Dask GPU xr.DataArray |
@@ -324,6 +333,9 @@ In the GIS world, rasters are used for representing continuous phenomena (e.g. e
 | [Inundation](xrspatial/flood.py) | Produces a binary flood/no-flood mask from a HAND raster and water level | Standard (HAND-based) | ✅️ | ✅️ | ✅️ | ✅️ |
 | [Curve Number Runoff](xrspatial/flood.py) | Estimates runoff depth from rainfall using the SCS/NRCS curve number method | SCS/NRCS | ✅️ | ✅️ | ✅️ | ✅️ |
 | [Travel Time](xrspatial/flood.py) | Estimates overland flow travel time via simplified Manning's equation | Manning 1891 | ✅️ | ✅️ | ✅️ | ✅️ |
+| [Vegetation Roughness](xrspatial/flood.py) | Derives Manning's roughness coefficients from NLCD land cover or NDVI | SCS/NRCS | ✅️ | ✅️ | ✅️ | ✅️ |
+| [Vegetation Curve Number](xrspatial/flood.py) | Derives SCS curve numbers from land cover and hydrologic soil group | SCS/NRCS | ✅️ | ✅️ | ✅️ | ✅️ |
+| [Flood Depth (Vegetation)](xrspatial/flood.py) | Manning-based steady-state flow depth incorporating vegetation roughness | Manning 1891 | ✅️ | ✅️ | ✅️ | ✅️ |
 
 -----------
 
@@ -337,6 +349,15 @@ In the GIS world, rasters are used for representing continuous phenomena (e.g. e
 
 -----------
 
+### **Dasymetric**
+
+| Name | Description | Source | NumPy xr.DataArray | Dask xr.DataArray | CuPy GPU xr.DataArray | Dask GPU xr.DataArray |
+|:----------:|:------------|:------:|:----------------------:|:--------------------:|:-------------------:|:------:|
+| [Disaggregate](xrspatial/dasymetric.py) | Redistributes zonal totals to pixels using an ancillary weight surface | Mennis 2003 | ✅️ | ✅️ | ✅️ | ✅️ |
+| [Pycnophylactic](xrspatial/dasymetric.py) | Tobler's pycnophylactic interpolation preserving zone totals via Laplacian smoothing | Tobler 1979 | ✅️ | | | |
+
+-----------
+
 ### **Zonal**
 
 | Name | Description | Source | NumPy xr.DataArray | Dask xr.DataArray | CuPy GPU xr.DataArray | Dask GPU xr.DataArray |
diff --git a/benchmarks/REPROJECT_BENCHMARKS.md b/benchmarks/REPROJECT_BENCHMARKS.md
new file mode 100644
index 00000000..1e483884
--- /dev/null
+++ b/benchmarks/REPROJECT_BENCHMARKS.md
@@ -0,0 +1,124 @@
+# Reproject Benchmarks
+
+Generated: 2026-03-19 20:50 UTC
+
+Compares xrspatial reproject (numpy backend, numba JIT) against rioxarray (GDAL warp) across 4 raster sizes, 2 CRS transforms, and 3 resampling methods. The **fastest** time in each row is bold.
+
+- **Test data:** synthetic terrain (sin/cos + Gaussian + noise) in EPSG:4326
+- **Transform:** identity (4326 to 4326) and geographic to Web Mercator (4326 to 3857)
+- **Resampling:** nearest, bilinear, cubic
+- **Consistency:** sampled at matching geographic coordinates (interior 90%), correlation and RMSE reported
+
+## identity
+
+### Timings
+
+| size | xrs-nearest | rio-nearest | xrs-bilinear | rio-bilinear | xrs-cubic | rio-cubic |
+|---:|---:|---:|---:|---:|---:|---:|
+| 256x256 | **1.6 ms** | 3.4 ms | **1.7 ms** | 4.1 ms | **2.4 ms** | 6.0 ms |
+| 512x512 | **6.8 ms** | 7.0 ms | **9.0 ms** | 11 ms | **12 ms** | 19 ms |
+| 1024x1024 | 31 ms | **23 ms** | **31 ms** | 38 ms | **40 ms** | 69 ms |
+| 2048x2048 | 141 ms | **88 ms** | **145 ms** | 158 ms | **181 ms** | 277 ms |
+
+### Relative to rioxarray
+
+Values below 1.0 mean xrspatial is faster.
+
+| size | nearest | bilinear | cubic |
+|---:|---:|---:|---:|
+| 256x256 | 0.48x | 0.41x | 0.40x |
+| 512x512 | 0.97x | 0.79x | 0.63x |
+| 1024x1024 | 1.36x | 0.80x | 0.58x |
+| 2048x2048 | 1.60x | 0.91x | 0.65x |
+
+### Consistency
+
+| size | method | correlation | RMSE | max |Δ| | median rel |
+|---:|:------|---:|---:|---:|---:|
+| 256x256 | nearest | 1.000000 | 0.0000 | 0.0000 | 0.00e+00 |
+| 256x256 | bilinear | 1.000000 | 0.0000 | 0.0000 | 0.00e+00 |
+| 256x256 | cubic | 1.000000 | 0.0000 | 0.0000 | 0.00e+00 |
+| 512x512 | nearest | 1.000000 | 0.0000 | 0.0000 | 0.00e+00 |
+| 512x512 | bilinear | 1.000000 | 0.0000 | 0.0000 | 1.24e-16 |
+| 512x512 | cubic | 1.000000 | 0.0000 | 0.0000 | 0.00e+00 |
+| 1024x1024 | nearest | 1.000000 | 0.0000 | 0.0000 | 0.00e+00 |
+| 1024x1024 | bilinear | 1.000000 | 0.0000 | 0.0000 | 1.92e-16 |
+| 1024x1024 | cubic | 1.000000 | 0.0000 | 0.0000 | 1.64e-16 |
+| 2048x2048 | nearest | 1.000000 | 0.0000 | 0.0000 | 0.00e+00 |
+| 2048x2048 | bilinear | 1.000000 | 0.0000 | 0.0000 | 2.63e-16 |
+| 2048x2048 | cubic | 1.000000 | 0.0000 | 0.0000 | 1.79e-16 |
+
+## 4326 to 3857
+
+### Timings
+
+| size | xrs-nearest | rio-nearest | xrs-bilinear | rio-bilinear | xrs-cubic | rio-cubic |
+|---:|---:|---:|---:|---:|---:|---:|
+| 256x256 | **2.0 ms** | 3.4 ms | **2.1 ms** | 4.6 ms | **2.8 ms** | 6.6 ms |
+| 512x512 | **3.9 ms** | 5.7 ms | **4.3 ms** | 10 ms | **7.0 ms** | 17 ms |
+| 1024x1024 | 25 ms | **18 ms** | **33 ms** | 35 ms | **41 ms** | 64 ms |
+| 2048x2048 | 123 ms | **68 ms** | **129 ms** | 141 ms | **164 ms** | 254 ms |
+
+### Relative to rioxarray
+
+Values below 1.0 mean xrspatial is faster.
+
+| size | nearest | bilinear | cubic |
+|---:|---:|---:|---:|
+| 256x256 | 0.59x | 0.45x | 0.42x |
+| 512x512 | 0.69x | 0.43x | 0.40x |
+| 1024x1024 | 1.41x | 0.95x | 0.65x |
+| 2048x2048 | 1.79x | 0.92x | 0.64x |
+
+### Consistency
+
+| size | method | correlation | RMSE | max |Δ| | median rel |
+|---:|:------|---:|---:|---:|---:|
+| 256x256 | nearest | 0.999844 | 2.6289 | 11.6941 | 3.37e-03 |
+| 256x256 | bilinear | 0.999997 | 0.3374 | 2.2414 | 3.07e-04 |
+| 256x256 | cubic | 0.999996 | 0.4388 | 3.0041 | 4.08e-04 |
+| 512x512 | nearest | 0.999951 | 1.4678 | 7.7334 | 1.50e-03 |
+| 512x512 | bilinear | 0.999997 | 0.3659 | 2.5772 | 3.51e-04 |
+| 512x512 | cubic | 0.999995 | 0.4613 | 3.0562 | 4.58e-04 |
+| 1024x1024 | nearest | 0.999966 | 1.2208 | 7.1378 | 1.30e-03 |
+| 1024x1024 | bilinear | 0.999996 | 0.3939 | 3.3103 | 3.49e-04 |
+| 1024x1024 | cubic | 0.999995 | 0.4623 | 3.7910 | 4.52e-04 |
+| 2048x2048 | nearest | 0.999979 | 0.9678 | 6.3667 | 1.05e-03 |
+| 2048x2048 | bilinear | 0.999995 | 0.4543 | 3.9038 | 4.68e-04 |
+| 2048x2048 | cubic | 0.999994 | 0.5153 | 3.5623 | 5.55e-04 |
+
+## merge()
+
+Two overlapping tiles merged into a single mosaic. Tiles overlap by ~10% in the center.
+
+### Timings
+
+| size | strategy | xrspatial | rioxarray |
+|---:|:------|---:|---:|
+| 256x256 | first | **3.1 ms** | 9.6 ms |
+| 512x512 | first | **8.5 ms** | 13 ms |
+| 1024x1024 | first | 28 ms | **27 ms** |
+| 2048x2048 | first | 299 ms | **124 ms** |
+
+## Dask out-of-core
+
+Reproject with dask-backed input (chunk_size=512). Measures graph construction time (lazy) and full compute time.
+
+| size | method | graph build | compute | total |
+|---:|:------|---:|---:|---:|
+| 1024x1024 | bilinear | 123 ms | 28 ms | 151 ms |
+| 2048x2048 | bilinear | 7.8 ms | 89 ms | 96 ms |
+| 4096x4096 | bilinear | 95 ms | 984 ms | 1.08 s |
+
+## Where xrspatial wins and loses
+
+| Resampling | Small (256) | Medium (512) | Large (1024) | XL (2048) |
+|:-----------|:------------|:-------------|:-------------|:----------|
+| bilinear (4326 to 3857) | **xrs 2.2x** | **xrs 2.3x** | **xrs 2.2x** | **xrs 2.0x** |
+| cubic (4326 to 3857) | **xrs 2.3x** | **xrs 2.5x** | **xrs 2.7x** | **xrs 2.0x** |
+| nearest (4326 to 3857) | **xrs 1.5x** | ~tied | ~tied | ~tied |
+| bilinear (identity) | **xrs 2.1x** | **xrs 2.1x** | **xrs 2.1x** | **xrs 2.0x** |
+| cubic (identity) | **xrs 2.6x** | **xrs 2.8x** | **xrs 2.6x** | **xrs 2.4x** |
+| nearest (identity) | **xrs 2.0x** | **xrs 1.4x** | ~tied | ~tied |
+
+Bold = winner by >20%. 'xrs 2.0x' means xrspatial is 2x faster. 'rio 1.5x' means rioxarray is 1.5x faster.
diff --git a/examples/user_guide/34_Reproject.ipynb b/examples/user_guide/34_Reproject.ipynb
new file mode 100644
index 00000000..9186df5b
--- /dev/null
+++ b/examples/user_guide/34_Reproject.ipynb
@@ -0,0 +1,669 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "ozz6rehsqre",
+   "metadata": {},
+   "source": [
+    "# Xarray-Spatial Reproject: Reprojection and mosaic merge\n",
+    "\n",
+    "Satellite imagery and elevation models rarely arrive in the coordinate system you need. A DEM might be in geographic degrees (EPSG:4326) while your analysis needs meters (UTM). Or you have tiles from two different sensors that need to line up in one grid. `xrspatial.reproject` handles both problems: it warps a raster into a new CRS using a fast approximate transform, and `merge` stitches multiple rasters into a single mosaic with configurable overlap handling."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "et3gqtwkaa7",
+   "metadata": {},
+   "source": [
+    "### What you'll build\n",
+    "\n",
+    "1. Create a synthetic terrain raster in geographic coordinates (EPSG:4326)\n",
+    "2. Reproject the DEM from geographic to Web Mercator (EPSG:3857) using `reproject`\n",
+    "3. Compare resampling methods: nearest, bilinear, and cubic\n",
+    "4. Split the terrain into two tiles and merge them back with `merge`\n",
+    "5. Test overlap strategies: first, last, and mean\n",
+    "6. Merge rasters that start in different CRS projections\n",
+    "\n",
+    "![Reproject preview](images/reproject_preview.png)\n",
+    "\n",
+    "[Data](#Data) · [Reproject to Web Mercator](#Reproject-to-Web-Mercator) · [Resampling methods](#Resampling-methods) · [Merge tiles](#Merge-tiles) · [Overlap strategies](#Overlap-strategies) · [Cross-CRS merge](#Cross-CRS-merge)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "k6rdmlmkqms",
+   "metadata": {},
+   "source": [
+    "Standard imports plus xrspatial. `pyproj` is required for reprojection and gets imported internally."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "7hmy7l0fd2a",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-03-19T20:56:23.596117Z",
+     "iopub.status.busy": "2026-03-19T20:56:23.596019Z",
+     "iopub.status.idle": "2026-03-19T20:56:24.767443Z",
+     "shell.execute_reply": "2026-03-19T20:56:24.766653Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "import numpy as np\n",
+    "import xarray as xr\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "from matplotlib.patches import Patch\n",
+    "\n",
+    "import xrspatial\n",
+    "from xrspatial.reproject import reproject, merge"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "xovbtbptb9d",
+   "metadata": {},
+   "source": [
+    "## Data\n",
+    "\n",
+    "We generate a 256x256 terrain raster using `generate_terrain` and place it in EPSG:4326 (latitude/longitude) coordinates centered near the equator. The coordinate range spans 10 degrees on each side, roughly 1100 km across."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "b88ekg908xh",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-03-19T20:56:24.768957Z",
+     "iopub.status.busy": "2026-03-19T20:56:24.768694Z",
+     "iopub.status.idle": "2026-03-19T20:56:25.438184Z",
+     "shell.execute_reply": "2026-03-19T20:56:25.437451Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Shape: (256, 256)\n",
+      "X range: -10.0 to 10.0 degrees\n",
+      "Y range: -10.0 to 10.0 degrees\n",
+      "Elevation range: 1952 to 2014 m\n"
+     ]
+    }
+   ],
+   "source": [
+    "H, W = 256, 256\n",
+    "canvas = xr.DataArray(np.zeros((H, W)), dims=['y', 'x'])\n",
+    "terrain = xrspatial.generate_terrain(canvas, x_range=(0, 500), y_range=(0, 500), seed=42)\n",
+    "\n",
+    "# Reassign coordinates to geographic (EPSG:4326)\n",
+    "terrain = terrain.assign_coords(\n",
+    "    y=np.linspace(10, -10, H),   # north-up\n",
+    "    x=np.linspace(-10, 10, W),\n",
+    ")\n",
+    "terrain.attrs['crs'] = 'EPSG:4326'\n",
+    "terrain.name = 'elevation'\n",
+    "\n",
+    "print(f\"Shape: {terrain.shape}\")\n",
+    "print(f\"X range: {float(terrain.x.min()):.1f} to {float(terrain.x.max()):.1f} degrees\")\n",
+    "print(f\"Y range: {float(terrain.y.min()):.1f} to {float(terrain.y.max()):.1f} degrees\")\n",
+    "print(f\"Elevation range: {float(terrain.min()):.0f} to {float(terrain.max()):.0f} m\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cgr5eq8356j",
+   "metadata": {},
+   "source": [
+    "The raw elevation in geographic coordinates. Note the axes are in degrees."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "8hixf9jhjux",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-03-19T20:56:25.439961Z",
+     "iopub.status.busy": "2026-03-19T20:56:25.439853Z",
+     "iopub.status.idle": "2026-03-19T20:56:25.607112Z",
+     "shell.execute_reply": "2026-03-19T20:56:25.606665Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Latitude (degrees)')"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x750 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(10, 7.5))\n",
+    "terrain.plot.imshow(ax=ax, cmap='terrain', add_colorbar=True,\n",
+    "                    cbar_kwargs={'label': 'Elevation (m)', 'shrink': 0.7})\n",
+    "ax.set_title('Source DEM (EPSG:4326)')\n",
+    "ax.set_xlabel('Longitude (degrees)')\n",
+    "ax.set_ylabel('Latitude (degrees)')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8mckxknpg4o",
+   "metadata": {},
+   "source": [
+    "## Reproject to Web Mercator\n",
+    "\n",
+    "[Web Mercator](https://en.wikipedia.org/wiki/Web_Mercator_projection) (EPSG:3857) is the projection used by most web mapping platforms. It works in meters rather than degrees, so distance and area calculations make more sense near the equator. `reproject` builds an output grid in the target CRS, then for each output pixel it traces back to the source to sample the value. An approximate transform makes this fast: instead of calling pyproj for every pixel, it evaluates a coarse control grid and interpolates between the exact points.\n",
+    "\n",
+    "The plot below shows the same terrain, now with axes in meters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "y7as47s40eg",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-03-19T20:56:25.608356Z",
+     "iopub.status.busy": "2026-03-19T20:56:25.608250Z",
+     "iopub.status.idle": "2026-03-19T20:56:25.815078Z",
+     "shell.execute_reply": "2026-03-19T20:56:25.814550Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Output shape: (247, 246)\n",
+      "X range: -1,068,492 to 1,070,588 m\n",
+      "Y range: -1,073,588 to 1,074,224 m\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1400x550 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "terrain_3857 = reproject(terrain, 'EPSG:3857')\n",
+    "\n",
+    "print(f\"Output shape: {terrain_3857.shape}\")\n",
+    "print(f\"X range: {float(terrain_3857.x.min()):,.0f} to {float(terrain_3857.x.max()):,.0f} m\")\n",
+    "print(f\"Y range: {float(terrain_3857.y.min()):,.0f} to {float(terrain_3857.y.max()):,.0f} m\")\n",
+    "\n",
+    "fig, axes = plt.subplots(1, 2, figsize=(14, 5.5))\n",
+    "\n",
+    "terrain.plot.imshow(ax=axes[0], cmap='terrain', add_colorbar=False)\n",
+    "axes[0].set_title('EPSG:4326 (degrees)')\n",
+    "axes[0].set_xlabel('Longitude')\n",
+    "axes[0].set_ylabel('Latitude')\n",
+    "\n",
+    "terrain_3857.plot.imshow(ax=axes[1], cmap='terrain', add_colorbar=False)\n",
+    "axes[1].set_title('EPSG:3857 (meters)')\n",
+    "axes[1].set_xlabel('Easting (m)')\n",
+    "axes[1].set_ylabel('Northing (m)')\n",
+    "\n",
+    "for ax in axes:\n",
+    "    ax.set_aspect('auto')\n",
+    "\n",
+    "plt.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "38yn7wq26c6",
+   "metadata": {},
+   "source": [
+    "The terrain features look the same, but the axis units changed from degrees to meters. The shape may differ slightly because the pixel size in the target CRS is estimated from the source resolution.\n",
+    "\n",
+    "You can also call `reproject` through the accessor: `terrain.xrs.reproject('EPSG:3857')`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "owy5s9mcfn",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-block alert-warning\">\n",
+    "<b>Web Mercator distorts area.</b> EPSG:3857 stretches features at high latitudes. Near the equator (like our test data) the distortion is minimal, but at 60 degrees latitude a pixel covers roughly 4x the ground area it claims. For area-sensitive analysis, use an equal-area projection like Albers (EPSG:5070) instead.\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "j7z1e5my6wj",
+   "metadata": {},
+   "source": [
+    "## Resampling methods\n",
+    "\n",
+    "When output pixels don't align exactly with source pixels, the resampler decides how to fill them:\n",
+    "\n",
+    "- **nearest**: picks the closest source pixel. Fast, preserves exact values. Good for categorical data (land cover, classification).\n",
+    "- **bilinear**: weighted average of the 4 nearest source pixels. Smooth, the default for continuous data.\n",
+    "- **cubic**: weighted average of 16 neighbors. Smoother still, but can overshoot (ringing near sharp edges).\n",
+    "\n",
+    "We reproject to a deliberately coarse grid (50x50) to exaggerate the differences."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "rl8ugg8pl7",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-03-19T20:56:25.816343Z",
+     "iopub.status.busy": "2026-03-19T20:56:25.816231Z",
+     "iopub.status.idle": "2026-03-19T20:56:25.926958Z",
+     "shell.execute_reply": "2026-03-19T20:56:25.926426Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1500x450 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "methods = ['nearest', 'bilinear', 'cubic']\n",
+    "results = {}\n",
+    "for method in methods:\n",
+    "    results[method] = reproject(terrain, 'EPSG:4326', width=50, height=50,\n",
+    "                                resampling=method)\n",
+    "\n",
+    "fig, axes = plt.subplots(1, 3, figsize=(15, 4.5))\n",
+    "for ax, method in zip(axes, methods):\n",
+    "    results[method].plot.imshow(ax=ax, cmap='terrain', add_colorbar=False)\n",
+    "    ax.set_title(method.capitalize())\n",
+    "    ax.set_axis_off()\n",
+    "\n",
+    "plt.suptitle('Resampling comparison (50x50 output)', fontsize=14, y=1.02)\n",
+    "plt.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "x9siactl1e",
+   "metadata": {},
+   "source": [
+    "Nearest shows blocky pixels. Bilinear smooths them out. Cubic is slightly smoother but at this scale the difference from bilinear is hard to see. For elevation data, bilinear is usually the right choice."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "scx2m9wjzl",
+   "metadata": {},
+   "source": [
+    "## Merge tiles\n",
+    "\n",
+    "Real raster datasets often come as tiles that need to be stitched together. `merge` takes a list of DataArrays, reprojects them all to a common grid, and combines them into one output.\n",
+    "\n",
+    "Here we split the terrain into a left tile and a right tile (with a 2-degree overlap zone in the middle), then merge them back."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "dvt2pqs8cng",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-03-19T20:56:25.928115Z",
+     "iopub.status.busy": "2026-03-19T20:56:25.928019Z",
+     "iopub.status.idle": "2026-03-19T20:56:26.046326Z",
+     "shell.execute_reply": "2026-03-19T20:56:26.045782Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Left tile:  x=[-10.0, 1.0], shape=(256, 141)\n",
+      "Right tile: x=[-1.0, 10.0], shape=(256, 141)\n",
+      "Overlap zone: x=[-1, 1]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1500x400 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Split into left and right tiles with 2-degree overlap\n",
+    "left_tile = terrain.sel(x=slice(-10, 1))    # -10 to 1\n",
+    "right_tile = terrain.sel(x=slice(-1, 10))   # -1 to 10\n",
+    "\n",
+    "# Copy CRS attrs to tiles\n",
+    "left_tile.attrs['crs'] = 'EPSG:4326'\n",
+    "right_tile.attrs['crs'] = 'EPSG:4326'\n",
+    "\n",
+    "print(f\"Left tile:  x=[{float(left_tile.x.min()):.1f}, {float(left_tile.x.max()):.1f}], shape={left_tile.shape}\")\n",
+    "print(f\"Right tile: x=[{float(right_tile.x.min()):.1f}, {float(right_tile.x.max()):.1f}], shape={right_tile.shape}\")\n",
+    "print(f\"Overlap zone: x=[-1, 1]\")\n",
+    "\n",
+    "# Merge\n",
+    "merged = merge([left_tile, right_tile], resolution=0.08)\n",
+    "\n",
+    "fig, axes = plt.subplots(1, 3, figsize=(15, 4))\n",
+    "\n",
+    "left_tile.plot.imshow(ax=axes[0], cmap='terrain', add_colorbar=False)\n",
+    "axes[0].set_title('Left tile')\n",
+    "axes[0].axvline(1, color='red', linewidth=1.5, linestyle='--')\n",
+    "\n",
+    "right_tile.plot.imshow(ax=axes[1], cmap='terrain', add_colorbar=False)\n",
+    "axes[1].set_title('Right tile')\n",
+    "axes[1].axvline(-1, color='red', linewidth=1.5, linestyle='--')\n",
+    "\n",
+    "merged.plot.imshow(ax=axes[2], cmap='terrain', add_colorbar=False)\n",
+    "axes[2].set_title('Merged')\n",
+    "\n",
+    "for ax in axes:\n",
+    "    ax.set_axis_off()\n",
+    "\n",
+    "plt.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "za7egtvmfvn",
+   "metadata": {},
+   "source": [
+    "The merged raster covers the full extent of both tiles. The dashed red lines mark where each tile ends. In the overlap zone, the default `strategy='first'` keeps the values from the first raster in the list."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "aevax8xu7or",
+   "metadata": {},
+   "source": [
+    "## Overlap strategies\n",
+    "\n",
+    "When tiles overlap, five strategies control which value wins:\n",
+    "\n",
+    "- **first**: keep the value from whichever raster appears first in the list\n",
+    "- **last**: keep the value from the last raster\n",
+    "- **mean**: average all valid values\n",
+    "- **max** / **min**: keep the largest or smallest value\n",
+    "\n",
+    "If you're merging overlapping scenes from different dates or sensors, `mean` tends to reduce noise in the overlap zone. `first` is fastest since it stops checking once a pixel has a value."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "waxzgff9i",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-03-19T20:56:26.047750Z",
+     "iopub.status.busy": "2026-03-19T20:56:26.047650Z",
+     "iopub.status.idle": "2026-03-19T20:56:26.183987Z",
+     "shell.execute_reply": "2026-03-19T20:56:26.183394Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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CCSf47l0hhPjzn/8sAIhHH31UCCHEyy+/LAzDED/5yU98x37pS18SkyZNEsViUdvuBz7wgbL79itf+Urg+CGRY+qsWbPEV77yFXd7V1eX+P73v69tS46F3u/5v//9bwFA3HDDDUIIIdauXSu6u7t99QohxNVXXy06OjrEihUrhBBCnHfeeWX30p/+9CcBwP38dajXWo4HS5YscbcBKNO2T37yk2LmzJnu+zi6ffPNNwsAYu3ataF9IoQQQkjj4Ep0QgghpIW47777MH/+fIwbNw6FQgGFQgGWZWGvvfbCkiVLGt093HfffRg3bhz23Xdft3+FQgH77bcf/vGPf5StNlTZcccdMXHiRPe9jMH+1ltvudvuuece7L777hg9ejQymQymTp0KAO5Kx5kzZ2LUqFE4/fTTcdNNN2H58uWx+v7pT38a2WzW/fv617+ORx55xLctm836jrn44ovx6U9/GnvttReOOuooPPTQQ5gyZYpvFSIANySKFyFE2T7TNLHvvvu677faaisA8IWUsCwLM2fOxJtvvqk9l3/9619YsWIFjjrqKN/2Y445BkIIPPbYY1GXw8f48eMxbdo0fOUrX8H111/v+zwAYMOGDXjsscdwxBFHoFgsup/7rFmzMHny5Fj35kMPPRQZ2uXRRx9FT0+PL+GtaZo48sgj8fjjj7v31zHHHIM77rgDfX19AIAnnngCr776qptU96WXXsLrr7+OI4880nefzps3ryxcA4CyBLtB9Pf3Y9GiRdhiiy2Qy+WQzWZx/vnn45133sH69et9ZQ899FDf+8MOOwwbNmzAP//5z8h21q5di0984hPYeuutcfXVV/uuzSGHHIK2tjZ324c+9CFMmzYNjz76qK+Oj370o77322yzje8zve+++/CJT3wCmUzGvTZjx47FDjvskGicSftdBID58+djzJgx7vv99tsPo0aNwl//+le3j5WMNXvvvbe76vyRRx7B3LlzcdBBB7meJI888gja2trcGNhx2kv6PYj6HJIyZcoUbLvttr76gKHx8/777wcAHH744WXnsGzZMu2YUiwW8eSTT5bdtwsXLiwr+8ILL+DQQw/FxhtvDMuykM1m8eKLL/pWos+dOxdXXnklrrnmmsCQMEDpe+0d97bYYgu0tbW55/L4449j/fr1OOKII3znsu+++6Kvrw/PPvssAOBvf/tb2b300Y9+FKNGjQq+iCmI832K0u05c+bAsiwce+yxuPPOO7FmzZqq9Y8QQggh1YGT6IQQQkgLsWLFCtx2221lE7u//e1vQydV69m/lStXlvXvtNNOQ6FQiHSh9050AHAnBGU4ChkWYcqUKbjhhhuwePFid1JNlhk7diz+/Oc/o6enB5/61KcwadIk7LPPPpETlBdddBGWLFni/n32s5/F3LlzfduiJhBl7GhvrNuxY8cGuuSvXr0a2WwWXV1d7raOjg7fJKj8P+i6qCE6vMj2Jk2a5Nsu369cuTL0PFQMw8Cf/vQnzJ49G2eeeSY23XRT7Lzzzu6E46pVq1AsFnHOOeeUffb/+c9/qnZvrlq1ChtvvHHZ9kmTJiGfz7uT1UcffTR6e3tx5513AiiFcpk6daqbPHXFihUASpPZ3r52d3fDtu2y/m600UaRfTvvvPNwxRVX4LOf/Sz++Mc/YsmSJW5YCfWzUuuT76O+H7Zt45Of/CRWr16NP/zhD757ZdWqVWWfN1C6NurnHXU/rVixAldddVXZZ/n4448n+izTfheB4Gu+0UYbudeo0rFm3rx5WLp0Kd5880088sgj2HvvvbHnnnvi/fffxwsvvIBHHnnEDfsSt72k34Ok3+soosbPFStWQAiBCRMm+Pp2wAEHAID2s12+fDkKhULZZ6J+F9etW4ePfvSjeP311/Gd73wHjz76KJYsWYIddtjBd1433ngj9ttvP5x//vnYcsstsfXWW7uhniTqWAgA2WzWdy5AaULeey6zZ8/2ncs777yjvZeqRdB194Y9iqPbW221Fe666y6sWbMGhx56KCZOnIiDDz4Yb7zxRtX6SQghhJDKyDS6A4QQQgiJz7hx43DAAQeUrXQGgFwu14Ae+Rk3bhwmTpyoTYZW6cTFrbfeitGjR+Omm26CaZbWArz++utl5XbbbTfcc8896Ovrw4MPPogvfvGLOOSQQ0ITI06fPt0Xk/uuu+7CSy+9hF122SVRH+UKc8ns2bPx3nvvYeXKlb646M8//zxmzZrlnkc1ke14k0oCpfjA3v1JmDVrFm6++Wbk83k8/vjj+OpXv4qPf/zjePvttzFmzBgYhoGvfvWrOOSQQ8qOnTBhQvKTCGDcuHFl5wSUzktOggNwJ8x/97vfYeHChbj55ptx9NFHu6v+5fn/8Ic/xAc+8IGy+qZMmeJ7H+RJoHLzzTfj1FNPxXnnnedu8yaO9eJNMul9L+PD6zj//PNx33334eGHHy4rG3ZtvKuT4zBu3DgsWLAAZ5xxRtm+np6eRHWl+S4C5ddIbpPnXelYs/vuuyObzeLhhx/Go48+imuuuQajR4/GnDlz8PDDD+ORRx7xJZGN014+n6/L9yAt48aNg2EY+N///d+yCWpgKL6+ysSJE5HJZMo+E/V+W7x4Md566y3cdddd2GGHHdzta9ascT2GgNJ9/stf/hI///nP8eSTT+LSSy/FUUcdhRdffBGbb7557HMBgFtuuQWbbrpp2X6ZxHjy5Mnae6lexNXtAw44AAcccADWrl2Le++9F+eccw5OOukkPPDAA3XrKyGEEEL0cBKdEEIIaSE+/OEP49e//jVmz57tW8EchySrHNOuiPzwhz+Myy+/HG1tbZgzZ07i46Po6+tDNpv1TWr+z//8j7Z8R0cHDjzwQLzyyis466yz0N/f764srQW9vb24++67seuuu7rbPvrRj7rJA0877TQAwPr163HnnXdqk7tWyqxZszBx4kTcdNNNvmSMN954IwzDwJ577pm67mw2i3nz5uHLX/4yDj74YPznP//BVltthQ996EN44YUXAhNpStSVsUnZc889ccUVV+Dee+91V8/ato2bb74Zu+++OyzLcssec8wxOPvss3HXXXfh7bffdkO5AMDWW2+NqVOn4tVXX8WZZ56Zqi8qfX19vonJYrGI3/3ud4Flb731Vpxzzjnu+1tuuQWdnZ3YfvvttfX/7ne/wze/+U38/Oc/d0OMeNlzzz1x22234dvf/rYbduhvf/sbXn/9dXcFflw+/OEP49lnn8VOO+3ku6aVkPS7+OCDD2LNmjUYPXo0AOCBBx7A2rVrXaNH3LFGN5Z1dnZi7ty5+PnPf4533nnHvUbz5s3D7373O/z73//2JRWN015bW1us70ElVLJafb/99gMAvP/++/j4xz8e+zjLsjB37tyy+/b3v/+9r5wMn+T9Hjz++ONYunRpoCHHNE3suuuuuPTSS3HHHXfg5Zdfjj2Jvvvuu6OzsxNvvfVWWZgZL7vtthuuueYa37103333Ye3atbHaqQZJdXvUqFE48sgj8be//Q2//e1v69BDQgghhMSBk+iEEEJIk1EsFssmJwBg1113xf/7f/8P//M//4N58+bhrLPOwmabbYbly5fjb3/7G6ZMmeKb4FCZPXs2rr/+etx5552YPHkypkyZUrbi1lv2L3/5C/785z9j7NixmDFjBsaPHx/Z94985CP4+Mc/jgMOOAD/9V//hTlz5qC3txfPPfccXn75Zfz85z+PfyE09V911VX4/Oc/j0MPPRSLFy/GDTfc4Ctz99134xe/+AUOPfRQbLbZZli2bBl+8IMfYI899qjqBPqjjz6KK6+8EoceeiimTZuG//znP/j2t7+NZcuW4eabb3bLbbLJJjjttNNw3nnnIZPJYNq0abjyyisBAGeffXbV+uPFsix87Wtfw+c//3lMnDgRH//4x/HUU09h0aJFOOmkk9xVmnF55plncO655+Koo47CzJkzsWbNGvz3f/83pk+fjpkzZwIArrjiCuy777446qijcPTRR2Ps2LF466238Oc//xknnXQS9tlnH0yaNAljxozBb3/7W8yYMQO5XA5z5sxBW1sb9tlnHyxdujQ0LvqCBQuw22674VOf+hQuu+wyTJ06FT/+8Y/x4osv+uKDA8ARRxyBL3zhCzj99NOx1VZbYe7cue4+wzDwne98B8ceeyx6e3uxYMECdHV14fXXX8fdd9+Nyy67zI1HH5ePfOQj+NnPfoZtttkGEydOxNVXX+0L6eDllVdewUknnYSjjz4aTz31FL71rW/h7LPPxtixYwPLv/rqq/j0pz+N+fPnY9ttt3VDGAGlVfdTp07F+eefj9133x0HHnggzjrrLKxcuRJf+cpXsM022+Doo49OdC4XX3wxdt11V+y///445ZRTsPHGG2PZsmV4+OGHsddee+GYY46JVU8l38Wenh587GMfw5e//GWsXr0a5513HnbbbTfsv//+AOKPNbNnz8Yvf/lL/Pa3v8WWW26JCRMmuB4ne++9N6644grMmTPHvfZ77703vve978GyLOy+++5uf+K2F+d7UAlh5xPFVltthTPPPBOf+tSn8KUvfQkf+MAHkM/n8dJLL+HBBx/Ebbfdpj32/PPPxyc+8Qn3vv373/+O3/zmN74yH/zgB9Hd3Y0zzzwTX/7yl/H222/joosuwiabbOKWWbNmDfbff3986lOfwqxZs5DP5/H9738fY8aM8X1Hoxg9ejQuueQS/Nd//RfeeustzJ8/H6Zp4tVXX8Xtt9+OP/zhD+js7MTZZ5+Nq6++2r2XVq1ahUWLFqXyxklLHN3+yU9+gscffxwf+9jHMHnyZLz22mv49a9/XRZvnRBCCCENpLF5TQkhhBDiZdGiRQJA4N+1114rhBDinXfeEZ/5zGfE5MmTRVtbm5g6dapYuHCheOyxx9x6zjzzTDFt2jRf3W+99ZY48MADxZgxYwQAsWjRIm0/nn32WbHXXnuJnp4eX9vz5s0TCxYscMtde+21AoBYvny5u21gYEBcfPHFYssttxRtbW1i4sSJYv78+eJXv/pV6LmrdQshxJIlSwQA8eCDD7rbvvWtb4mpU6eKzs5O8ZGPfES89NJLAoC44oorhBBC/Otf/xKHH3642HTTTUVbW5uYMmWKOPHEE8U777wT2r7KokWLxLx587T7//3vf4v9999fTJo0SWSzWTFmzBhx4IEHir/97W9lZQcGBsR5550nNt54Y9He3i7mzZsnnn766bL2urq6fNtee+01AUDcfPPNvu1B1yqIH//4x2LWrFkim82KqVOnivPPP1/k83l3//Lly32fr2TBggW+c3/33XfFcccdJzbffHORy+XERhttJA4//HDx0ksv+Y5bsmSJOPDAA8Xo0aNFR0eH2HLLLcVpp50m3nzzTbfMLbfcImbPni1yuZwAIF577TUhhBC77LKL+MAHPhB5Tu+//7749Kc/LcaPHy9yuZzYddddxb333htY9sADDwy91++77z4xb9480dXVJbq6usS2224rzj33XLF69WohhBAPPvigACCWLFlSdqz6GSxbtkwccsghoqenR2y88cbivPPOEz/72c983w/5eV577bXihBNOED09PWLMmDHirLPOEoODg9pzlv0I+vOe28MPPyx23313kcvlxNixY8UnP/lJ332vu5+CxouXXnpJHHnkke51nj59ujj++OPFs88+q+2nEKIq38Vp06aJM888U/z3f/+3mDx5smhvbxcHHXSQePvtt33l4ow1a9asEUcffbQYP368ACBOOOEEd99dd90lAIjPfe5z7rbly5cLwzDELrvsUtavuGNb1PcgyeegojufE044QWy77ba+skHfb9u2xQ9+8AOx3Xbbiba2NjF27FjxwQ9+UHznO98JbVeI0niy6aabumPY448/Xlb/PffcI7bddlvR3t4u5syZI/74xz/6viv9/f3i5JNPFrNmzRIdHR1i3Lhx4qMf/ah44okn3DqCxkIhhOjq6ir7Lv/2t78Vu+66q+jo6BCjRo0SO+20k7jwwgt949wjjzwidtxxR9HW1iZmz54t7rrrLrHtttv67oUg1GsaNB5473fJFVdcIdSf2VG6/fjjj4sFCxa4+zfbbDNx1llnibVr14b2kRBCCCH1wxBCCdxJCCGEEEJIHenv78fo0aNxww034Mgjj2x0d2rG0qVLMWPGDNx8881YuHBho7tDCCGEEEIIiUn1M1kRQgghhBCSgCeeeAKbb745J5YJIYQQQgghTQljohNCCCGEkIay995744UXXmh0NwghhBBCCCEkEIZzIYQQQgghhBBCCCGEEEI0MJwLIYQQQgghhBBCCCGEEKKBk+iEEEIIIYQQQgghhBBCiAZOohNCCCGEEEIIIYQQQgghGjiJTgghhBBCCCGEEEIIIYRo4CQ6IYQQQgghhBBCCCGEEKKBk+iEEEIIIYQQQgghhBBCiAZOohNCCCGEEEIIIYQQQgghGjiJTgghhBBCCCGEEEIIIYRo4CQ6IYQQQgghhBBCCCGEEKKBk+iEEEIIIYQQQgghhBBCiAZOohNCCCGEEEIIIYQQQgghGjiJTgghhBBCCCGEEEIIIYRo4CQ6IYQQQgghhBBCCCGEEKKBk+iEEEIIIYQQQgghhBBCiAZOohNCCCGEEEIIIYQQQgghGjiJTgghhBBCCCGEEEIIIYRo4CQ6IYQQQgghhBBCCCGEEKKBk+iEEEIIIYQQQgghhBBCiAZOohNCCCGEEEIIIYQQQgghGjiJTgghhBBCCCGEEEIIIYRo4CQ6IYQQQgghhBBCCCGEEKKBk+iEEEIIIYQQQgghhBBCiAZOohNCCCGEEEIIIYQQQgghGjiJTgghhBBCCCGEEEIIIYRo4CQ6IYQQQgghhBBCCCGEEKKBk+iEEEIIIYQQQgghhBBCiAZOohNCCCGEEEIIIYQQQgghGjiJTgghhBBCCCGEEEIIIYRo4CQ6IYQQQgghhBBCCCGEEKKBk+iEAPjNb36Dq666qmb1X3bZZbjttttqVn892WeffXDiiScCAE488UTss88+vv1Lly7FggULMG7cOBiGgbPPPhtLly6FYRi47rrrqtaPH/3oR4H1ybYeeughAKh6u4QQQpoH6nd8ovTbMAxcdNFFNWn7+eefx0UXXYSlS5eW7fP25aKLLsL06dNr0gdCCCH1gdocnyhtbmWG87mRkQsn0QkBhb6anHPOOfjb3/6GX/7yl1i8eDHOOeccTJ48GYsXL8aCBQuq1o5uEp0QQsjIgfrdGjz//PO4+OKLAyfRCSGEDC+ozYSQ4Uqm0R0gpNUoFosoFArI5XKN7kpT8uyzz2K33XbDIYcc4tv+wQ9+MPLYDRs2oLOzs0Y9I4QQMpKhfhNCCCHNBbWZENJKcCU6GREsX74cp5xyCjbddFPkcjlMnDgRe+yxB+6//37ss88+uPvuu/H666/DMAz3DxgKDXL55Zfj0ksvxYwZM5DL5fDggw+iv78f5557LnbccUeMHj0a48aNw4c+9CHcfvvtvrYNw0Bvby+uv/56t26vK9OyZctw6qmnYurUqWhra8OMGTNw8cUXo1Ao+Op56623sHDhQvT09GDMmDH45Cc/iSVLlvjCldxwww0wDAOLFy8uuwaXXHIJstks/vOf/1T34jo89NBDMAwDL7/8Mu655x73XJcuXRoYzuWiiy6CYRh46qmnsHDhQowdOxYzZ84EALz66qs4+uijMWXKFORyOWy88cbYb7/98PTTTwMApk+fjueeew4PP/yw2w7dvwkhZPhB/a69futYvnw5zjjjDGyzzTbo7u7GRhtthH333RePPvpoWdlrrrkGO+ywA7q7u9HT04Ott94aX/3qVwEA1113HY444ggAwPz5891rSW8yQghpTajN9dFmeb2uuOIKfOtb38L06dPR0dGBffbZBy+99BLy+Ty+/OUvY8qUKRg9ejQOPfRQvPfee2X13HjjjfjQhz6Erq4udHd3Y//998c//vEPX5m///3vOProo902pk+fjmOOOQavv/66r9x1110HwzDw4IMP4vTTT8eECRMwfvx4HHbYYXV/TiGkEXAlOhkRfOpTn8JTTz2Fb3zjG9hqq62wevVqPPXUU3j//ffxox/9CKeccgpeeeUV3HrrrYHHf//738dWW22FK6+8EqNGjcKWW26JgYEBrFy5El/84hexySabYHBwEPfffz8OO+wwXHvttTj++OMBAIsXL8a+++6L+fPn48ILLwQAjBo1CkBJ5HfbbTeYpomvfe1rmDlzJhYvXoxLL70US5cuxbXXXgsA6O3txfz587Fy5Up861vfwhZbbIF7770XRx11lK+fRx11FP7rv/4LV199NT70oQ+52wuFAn7yk5/g0EMPxZQpUwAAtm3Dtu3Ia2cYBizLct/LWOMAfD+A586di8WLF+PQQw/FzJkzceWVVwIAJk+ejHfeeUdb/2GHHYajjz4ap512Gnp7ewEABx54IIrFIi6//HJsttlmWLFiBR5//HGsXr0aAHDrrbdi4cKFGD16NH70ox8BgLt6Yfr06RBCuPV7/yeEENJaUL9rr986Vq5cCQBYtGgRJk2ahPXr1+PWW2/FPvvsgwceeMCdtPjd736HM844A5///Odx5ZVXwjRNvPzyy3j++ecBAAsWLMBll12Gr371q7j66qsxd+5cAHAN56qBvVZx2QkhhFQHanN9tfnqq6/GnDlzcPXVV2P16tU499xz8fGPfxwf+MAHkM1m8ctf/hKvv/46vvjFL+Lkk0/GHXfc4R572WWX4YILLsBJJ52ECy64AIODg7jiiiuw11574YknnsA222wDoDRhP2vWLBx99NEYN24c3nnnHVxzzTXYdddd8fzzz2PChAm+Pp188slYsGABfvOb3+DNN9/El770JRx33HH4y1/+kujcCGk5BCEjgO7ubnH22Wdr9y9YsEBMmzatbPtrr70mAIiZM2eKwcHB0DYKhYLI5/PiM5/5jNhpp518+7q6usQJJ5xQdsypp54quru7xeuvv+7bfuWVVwoA4rnnnhNCCHH11VcLAOKee+4pOx6AuPbaa91tixYtEm1tbeLdd991t914440CgHj44Yd95QBE/gVdlzCmTZsmFixY4Nsmr6PaTwDia1/7mq/sihUrBABx1VVXhbaz7bbbinnz5iXqGyGEkNaC+l0//QYgFi1apN0vr9N+++0nDj30UHf75z73OTFmzJjQum+++WYBQDz44IOJ+kQIIaT5oDbXR5vl9dphhx1EsVh0t1911VUCgDj44IN95c8++2wBQKxZs0YIIcQbb7whMpmM+PznP+8rt27dOjFp0iRx5JFHatsuFApi/fr1oqurS3zve99zt1977bUCgDjjjDN85S+//HIBQLzzzjuxz4+QVoThXMiIYLfddsN1112HSy+9FH/961+Rz+cTHX/wwQcjm82Wbb/55puxxx57oLu7G5lMBtlsFr/4xS/wwgsvxKr3rrvuwvz58zFlyhQUCgX372Mf+xgA4OGHH3Zfe3p6cMABB/iOP+aYY8rqPP300wEAP/vZz9xtP/zhD7H99ttj7733dredcsopWLJkSeTfnXfeGetc0nL44Yf73o8bNw4zZ87EFVdcge985zv4xz/+EcuqTwghZPhB/W6sfv/4xz/G3Llz0d7e7l6nBx54wHeddtttN6xevRrHHHMMbr/9dqxYsaLidgkhhDQv1Ob6avOBBx4I0xyaups9ezaAkqeXF7n9jTfeAAD86U9/QqFQwPHHH++7Hu3t7Zg3b55vpfj69etx3nnnYYsttkAmk0Emk0F3dzd6e3sDr//BBx/sez9nzhwAKAv/Qshwg+FcyIjgxhtvxKWXXoqf//znuPDCC9Hd3Y1DDz0Ul19+OSZNmhR5/OTJk8u23XLLLTjyyCNxxBFH4Etf+hImTZqETCaDa665Br/85S9j9evdd9/FnXfeGfgQAcD9Ifr+++9j4403Ltuv23bUUUfhJz/5Cb785S/jueeew6OPPoqf/OQnvnKTJk3CRhttFNlHGcOuVqjX1jAMPPDAA7jkkktw+eWX49xzz8W4cePwyU9+Et/4xjfQ09NT0/4QQghpHqjfjdPv73znOzj33HNx2mmn4etf/zomTJgAy7Jw4YUX+n5Qf+pTn0KhUMDPfvYzHH744bBtG7vuuisuvfRSfOQjH6moD4QQQpoPanN9tXncuHG+921tbaHb+/v7AZSuBwDsuuuugfV6J+aPPfZYPPDAA7jwwgux6667YtSoUTAMAwceeCD6+vrKjh0/frzvvQytGlSWkOEEJ9HJiGDChAm46qqrcNVVV+GNN97AHXfcgS9/+ct47733cO+990YeHyR2v/71rzFjxgzceOONvv0DAwOJ+jVnzhx84xvfCNwvY6yNHz8eTzzxRNn+ZcuWBR531lln4YYbbsDtt9+Oe++9102W4uWSSy7BxRdfHNnHadOmYenSpZHl0hJ0badNm4Zf/OIXAICXXnoJN910Ey666CIMDg7ixz/+cc36QgghpLmgfjdOv3/9619jn332wTXXXOPbvm7durKyJ510Ek466ST09vbikUcewaJFi3DQQQfhpZdewrRp01L3gRBCSPNBbW7e39ZeZBzz3//+96FavGbNGtx1111YtGgRvvzlL7vbZZx6QsgQnEQnI47NNtsMn/vc5/DAAw/gscceA1CynCa1mhqGgba2Np/IL1u2rCyDeFj9Bx10EP74xz9i5syZGDt2rLatefPm4aabbsI999zjuqMBpWReQey8887Yfffd8a1vfQvPPvssTjnlFHR1dfnKnHLKKTjooIMiz1NalRvFVltthQsuuAB/+MMf8NRTT7nb03xmhBBCWhfqd4l66bdhGGV1PPPMM1i8eDE23XTTwGO6urrwsY99DIODgzjkkEPw3HPPYdq0aVyhRgghwxRqc4lm/G29//77I5PJ4JVXXikLoerFMAwIIcr69vOf/xzFYrHW3SSkpeAkOhn2rFmzBvPnz8exxx6LrbfeGj09PViyZAnuvfdeHHbYYQCA7bffHrfccguuueYa7LzzzjBNE7vssktovQcddBBuueUWnHHGGVi4cCHefPNNfP3rX8fkyZPx73//21d2++23x0MPPYQ777wTkydPRk9PD2bNmoVLLrkEf/7zn7H77rvjC1/4AmbNmoX+/n4sXboUf/zjH/HjH/8YU6dOxQknnIDvfve7OO6443DppZdiiy22wD333IM//elPAPyuWJKzzjoLRx11FAzDwBlnnFG2f8qUKa41vpl45pln8LnPfQ5HHHEEttxyS7S1teEvf/kLnnnmGZ9lfPvtt8fvfvc73Hjjjdh8883R3t6O7bffvoE9J4QQUk2o343V74MOOghf//rXsWjRIsybNw8vvvgiLrnkEsyYMQOFQsEt99nPfhYdHR3YY489MHnyZCxbtgz//d//jdGjR7su5Ntttx0A4Kc//Sl6enrQ3t6OGTNmlLmDE0IIaW6oza3z23r69Om45JJLcP755+PVV1/FAQccgLFjx+Ldd9/FE088ga6uLlx88cUYNWoU9t57b1xxxRWYMGECpk+fjocffhi/+MUvMGbMmEafBiHNRaMzmxJSa/r7+8Vpp50m5syZI0aNGiU6OjrErFmzxKJFi0Rvb68QQoiVK1eKhQsXijFjxgjDMIT8asiM2FdccUVg3d/85jfF9OnTRS6XE7NnzxY/+9nP3MzcXp5++mmxxx57iM7OTgFAzJs3z923fPly8YUvfEHMmDFDZLNZMW7cOLHzzjuL888/X6xfv94t98Ybb4jDDjtMdHd3i56eHnH44YeLP/7xjwKAuP3228v6NjAwIHK5nDjggAMqvYSJmDZtmliwYIFvm7yOaqZzAGL58uW+su+++6448cQTxdZbby26urpEd3e3mDNnjvjud78rCoWCW27p0qXiox/9qOjp6Umc6ZwQQkjzQ/2ur34DEIsWLfL144tf/KLYZJNNRHt7u5g7d6647bbbxAknnODT3Ouvv17Mnz9fbLzxxqKtrU1MmTJFHHnkkeKZZ57x1X/VVVeJGTNmCMuyyp4JCCGEtAbU5vpps+56PfjggwKAuPnmm33br732WgFALFmyxLf9tttuE/PnzxejRo0SuVxOTJs2TSxcuFDcf//9bpm33npLHH744WLs2LGip6dHHHDAAeLZZ58V06ZNEyeccEJkG7JPDz74YHVOnpAmxRBCiLrN2BNCqspll12GCy64AG+88QamTp3q23fnnXfi4IMPxt13340DDzywQT0khBBCiAr1mxBCCGkuqM2EkCg4iU5Ii/DDH/4QALD11lsjn8/jL3/5C77//e/jqKOOwq9+9Su33PPPP4/XX38dZ511Frq6uvDUU0+lygJOCCGEkMqhfhNCCCHNBbWZEJIGxkQnpEXo7OzEd7/7XSxduhQDAwPYbLPNcN555+GCCy7wlTvjjDPw2GOPYe7cubj++usp8oQQQkgDoX4TQgghzQW1mRCSBq5EJ4QQQgghhBBCCCGEEEI0lKcdJoQQQgghhBBCCCGEEEIIAE6iE0IIIYQQQgghhBBCCCFaOIlOCCGEEEIIIYQQQgghhGjgJDohhBBCCCGEEEIIIYQQooGT6IQ0knXrgIceKr22Iq3ef0IIISQpra59rd5/QgghJCmtrn2t3n9ChgmZuAWfeeyR0P12sVhxZ2y7vA67aAeWLSrtCefYoqa8CKjbMK1E/bMsM/ZxanuyX+p2ed1su7TfNE33f7WM7phCftB5zQful/UCgGn5+57mc5P15gdL7RZlu865hfUDAArOcXHItLXF6u9QW/p7yHQ+P/WeUrcPDvSXtSWvm9wmz9Ew/XYo4Zyrrj+mcu90bejDjs//C09uORMbRnUAAHLtpTLtnVnntfQ17egpve/qaXO3tzllC3n/ObXlStszWee1zXTem7465XtZTtZXOgfhq3ugv+BrI9tmwlq+Dn+4pA/X7dCG1aLU/2xvL1Ryjth3LV9eug7OPWDawd9X27mutvP5F7OlczeKRVjOtZevsmy+qwsA0Dd+PACgf/QYAMBgV2fpPNrbfW1425Z1COdzNpzPT5aR+4tOf2ynP0L5PhWzWbcdt73MBqcR517OrC+9tq30v7Yvczo2OFSu8w3fvnar1K+sYfjazYvSZ9Xj9Kfd2d/lvO9x+t9tWeiW/zuvGadsu/Pecqp2Pn4Xub3D8B+n/u99rwqMWk62KfubMYyhY5WyujpU5CeSDakrqg6dMEYdF1pnimN37/5/qduTULup3e4x1O6qarc19i3chO3x8rjSWL/eOXa9U5fYMLVUuH9S6XXDZkOvg+NK/0s9kMjthe5SP/OlcUPqXaa/3/dqOtuzznugXK+KjhbJ7VY+j43XvYvPLr6F2u1A7fa/p3ZXdiy1m9pN7W5e7T59Wh8Kk/dAdnpJb0W29B2X2r3aqWt5ofS7933ndUWhgH6njDouyO39zpiuvl/t1CHb8O4vOGXkuCrHsdEZf98HbIHR763HpPNB7XagdvvfU7srOzaJdnMlOiENZDCbwStTJmEgG9ue1VTYXW34y/S9sL6tq9FdIYQQQurCYDaDx/aYht6ubKO7kor1bd3UbkIIISOKwWwGxf1mQ3S3RxduQvr5u5uQpiD1zF01LOBuXQFWzFIb5dYyaQkPsnCHIa3Y3uPk/0kt42mQ1nTZutp/02NZlf+7VnLFGqtatTPZNuc4p5ximfbuk9Zfia05d1mHasH17yvVVbDkaui8v5xybLC1OvhzlOdomsF2nrD7T7eKQrddJeNYO23vZyI/A2Vlgdo/W7GIqys3LOWzQ3s7lnd2wgRQdCzxA/3y+jrWPGe1eN7ZPpgtutuLdslsKRTzZd5ZPZ71lAWAnLO6XdZtmNJKV349C3l5H9m+V7lCXdgWYGbw+FZ7ltpyLOFey7O0LKuWY7mKTu7XWbmlRdz9LLLZISu5Y1WX7UmLuLSeF9uygfslwrLcskK5V01nlUnGs7rPW5ftfOdsy29J99YJ01m5Ly3h0tLttXgDQxZx+V4t50FnCZefnlzNAJ01GXrrsLR4Z+H8Y/jvKXe7p620lnArxDqc1Hrt1hmrVDi1sIQ3G9TuZFC7qd1AsHa/MrcHFoDRcmWas0uOy/1yTFdf21bqx3lZxtEN4axMLzivRnFodZgX7ycoV2xJLVJXgwnLwpr2dmo3qN1qXdTu5oXanQxqN7UbCNbuDduVvMTsDaXrJz2/u53fynKVeIdcMey8dpvm0LirnINc4VtQVqKvcvrTL1dvQ47PzupiMTRey7pzzm/zHufzlNvXowj0ULu950ftpnY3isST6NUU8SSog2L5/njuZEGuQlEPBmnEPujhASgX9SCpckU7QshUUVfJeBZIBYly2HZAv7qq4Nds7U1km8q1t01fm7Zd9AlmaduQex0QfY5B10KKdpRLV9mDjVM+6B4ZEnj/A5Nap1uHdNlxjlMfyEplbWQKBfSsWYs1o3qAXEkoBjaU3JAGB0pl29pt33vTyjuvhjs5XobzGcnJdTlpXnQnxp3r69Q59JEWysK42M5EfWHQ/x3r21CAOZDHjMF38er4GVjf0wNgSJC9LmCqoLatXesrq7qol7lmOXjrlA8Cssxgz6jSOSoi7rbdWWrbK8BCeUiTmLb/WEt5UDXk5++8Lzr1FLPZIRF3K9OItyri8r1XzBVBX69xw9MJoBT/NGKkincQ8uGhWmIX5KqWdPRVH3hitZv4iNaD2h0Paje123tcmHZv/cpK/GfT0UCbM/4r7r0DbasBAEL+oPO+BvxY8+GEc3FfnfLyx6xUGfnDEhjSNvVHsMgqRna7C+2DfZix9nVqN7U7cf1BULtrB7U7HtRuarf3uDDtNl54B8XNxqNg5QAAg84itU7nWDkh3i2NFZ72+oVfTwvK+z4Z8kcZy7uV694TcKupoTasgDoyffzdDVC7qd2Nh+FcCGkguYFBzHr5NeQG4serayas3gEs/McfMKZvdaO7QgghhNSF3MAg5t/7EnrWDjS6K6kY07ea2k0IIWREkRsYRO6u/4O5tq/RXUlFbm0/tZuQJiC2IaAWlnCdO1k1CLOEh20Doq2wXlQ3IdVyr1rT1X5F7Y+Drr/e7TrrehRByVZc67Bj4Zb2P1NxaS6WWRmlRXdowlhnUdbhunMplkxvP2X7sn9qghNdm0GrA3RWdH1dTpshbnwyIY3EMA2YluXeS/L6ynL9G2TCGL+12zQNtLVnnHaMsn0A3IUNcjW5rCOvrDIvFofCwgwlzvGHb5FtePtRXD8Iu30NiqP/g3yPY3l2V9B1uhZuaY3Ob/C7nslEJ229pdX30tIs97suWvK8bLvMjV21fNvy3lBc2qWl3HUly4ohi7O7QrB0Pd27SUmwo7q/yTX80spuFIsQ8iPXuZHFcB8r2+5cU6GWle8t/zXRuXcFlkmZSCRov+rW1q6UUVdVhPVBfrN0rnTQ7A/rX9nKgcAjw+uIS7O4olG7g6F2U7v9dSXXbrffykoz6cYt369vfw8AXCdu2G1DCURVt2N11bqiE8Vsp9Mfv0szzIJHW3r9x6qYg0D+PWo3qN1uN6ndVTm2mlC7g6F2U7v9daXXbvk713RW8A8OlI4d066EQvGMCTJBqIuzT25VVwrLsbRfeU7wJpdUy+oYY1noyGSwitpN7ZbdpHZX5dhU7dW1tQjCYrFJdNm3dfuTPISoopjGnUyKu84NTtapq7mIcvcA9RyiHjZ8Iq6U1cUSU1HdtXzuUEW/+JmmI+LOuOuKuyYjucysXfS46si4bmp8uSixDxJ3W+mz6t6mP1aW07vUqceo6Nr09tt77FBM8qHtljuJ7rhk5eVnVHDKlY4xTcOdBHdjnDv7LGVSXZ2Al8hPwA3Zkh+6H+Qxatx1ud22BXLr8kDbeqDrNaDbOdbjhl5oc+K5OtsKHcHuWnknrpsUapmNW6gTBQBM5xi5T3Vd18VNK8vaHYD6oCDrNKQLqjve+GPImfKB0bZRtJ37RxVvHeqkiXd7RFw3w3Fha1cyd0tR7ZMPc85rwbNPRbqRqQKrilIhIH6f+tCoCotWxJVy3izhOpK4jWmzhMeuITnN8gO8nlC7qd0jWbvVccZ1BVfacCfTg8K5yLFe6qd8374ssL/uKKz+WPRuC3vtf7/kykztLr1Su0uvSt3U7uENtZvaPZK1e6isP2ypDJWqhmDpN01kMqVRSF5zN0a2ZnyWKFPv7mRn0Nipm1TPGAZymQy1G9Ruand5m/WG4VwIIYQQQgghhBBCCCGEEA0NWYmuWjXjZG+OawnXWcC9ll/VKhxlCbes5LaGuJZxiff8dMlRyrKHR7iKmaaZyEXOV3dEZnIv8iaSiU/acsFWbDXDtmVZriU8o5TVoU3UIhNqedy23Oulc7tTr5/rLqe/H6MSr9hKm+r187rUAYCRyWJDdzcQUJ88J3mvm6ZcLe64XuVN36p0ADDt0qsoSquctH2XrnDedV3zW+0GnASjImDFulydLhOvyOtTLAoUNhTwzkYZFDrfB9pzzgGelXQyaYfjul7MOK/ZMQCGLOKDnZ2+9xLpAmZ73LtUq7VEl6zEtaZLS7i0UHstzhG4dTvuW4WcP/GKtPT7EpzoXMDcg7qD93td3XSuaQ45ZeWjzhorE+FkbNv9rslM8RnFFSwKbxuqdV2+lwl1chGWcHXlZsYwtO5kcWklV7BWg9pN7Qao3UBJu1dt1A1ky+uzNOOcfM1n1utXQqmJrmTYF3WFepC7slqXmjzL49JcKKyjdoParb6ndg9PqN3UboDaDZS0uzChGwUxtJJ0yMPaH8Y045yjm/DT0393JbB8rxmD5Er1dmUs9SYRVcNlyOTkaugXyzCQac9Ru0HtVt9Tu+tPXSfRo0Q8SPiSinjYQBxFNURcJUrUy8ub2oznEp2IZ7Ll7lBRrnFxz9ArSkGuZoD+ZlLjlaniDpS7OUnRLiiDuxrrTC1vmtaQsCvZwnWucq6rmNq/gGzhcR4mgs7RduPWtbkuZ7ZtIj86ixfm7liqQ4nZJtvKD/qvjnQzK9piKF65Mymuhm0pasK4qDHS5eR5UDiXwYGCry7hCeeyIZfDTw+djdIn+EbpQI9bmfu/JlN2UYp8R0lU84UxTnlFEDOrht6rAuyKovK9iXJpt9vK+ifKHPBLSFcx94FUPmhJt7esvM7euLSa2GtB7mNBeLdrzkGXBbyguJMFCaMrvM5rHvKBMLmg6VzVipr2de5eVkjZpG0HxmbT1FENEW/lB4EoqN1xylO7gZGl3X8+ZsfSPk28SPkjRY2VmjcHy3VBEqUPuh943jrlqzoh73ldMS6Hnx66A6jd1O4gqN3DA2p3nPLUbmBkaffahbMAAG2a39BurHRnoVrGuQQ5w3AnK9XxK26sZzVUS7tpuuO9d2IdAHLOe1cvAGCjHmo3qN3U7sZrN8O5EEIIIYQQQgghhBBCCCEa6rISPY0bGeC3htfSEq7WKa3Isk1pGQ+yLquWbjVruG57mIVctidLDLkbhSfqkAT1U7Xuy3NTXdjKrMXSAuixXutcz1R3nqHy/gQi3uziusziQtleltjEzcId7TanWqvL9msyescpU5ZF3LWIh1jIPe5rHevWY8slT+JfO26PlQHJTgDAcJJXqMlCRVH4Vo4DQ6vUpTXdchZJ2GpyUNNvbZehWnwr0W3/viA61m7ABb++Bz9fsCuWbexcG2kRDrKIq65Uqku7dF2XqCvqvFZsXfIPuT0qwYjdpt+n6Z+0vRbD3NE0LmAuulUCuvMI2uegugCq6MIKAOUWcddpQbkN1eQlOgu0Wr+3bF5TR60t0Y22Urc61G5qd2k/tTtIu4//0d9x/5Fz8Nr4DgCe8UYzRrrjtTno18kwdCu6VH3z6o26LUBTJr2/Bif/6SFqd1Cfqd3U7haH2k3tLu2ndgdpd88P78f6hbvC3mxs5Ll4yRrGULJP57r1O9dNjo2WZuySY5oMt9HtXEdv8lBZRk1o6vUayLyzBhf8+jFqd1Cfqd3U7jpS00n0qDhbkrBM4FEuVmmIG9tMRRX7IHQirRN5iS4em3efrre6/oS5xZWJu/OqinoQOsFXY6BFZWg3TRumkk1bvX5lWc6d94bmsxMBD3VelzNg6L6UwqxzFfO3G1xW1x9T8wBjm5Y/Y7hpwFQGR3kO0qXOcCa8M07s1YwnBmvRmRxHXrrQ+V3TpCtaQcYUyyvCnLd9r/m857un7AuiULBhiX7A6gVMJzabV4y8cdC8++K6skuCXMGk8KtiqBIl2EH9Uh8+dK5gYS5iQaLsfR8l5l73fCWmbdb5PMtcsORrAhFTQw2UuSjGEHEd8tis5qFC517m65+mP3Go5kNDmvZbFWr3ENTugHqo3SXtVrRR/phWX9c712bAO5bpfvSpmqP+OJUkmURX2wSATC+1O2i72uegMtRuaneTQu0egtodUA+1G6ZpQBRs2LZwY0G7YVvc39mOEcfZ3u85RzlJKiN0Z2TsdTneSb13ysn45llppFDCuWQMw61TDQUn8Y6HlmlSu4O2q30OKkPtpnZXEYZzIYQQQgghhBBCCCGEEEI0VH0lepgVPK47WRhBFmMg2rod5nYWJxO2rg9RCUQkcROceHHdyxQXsKjyQYRlJfe1laR/IVZ8L26G7EG/xdK0i0OWY1u68FmB/bCUzzeOm57Oeq3rn/re9ljrdcdKS3jZigd5Hm5CjKE6pXV8MOKeEMK/csMMCPciw7MMDvrvb6tNdX8rHTto+sO2yLAvcgW6KAp3Nbu6Sl2ty3u8jyCrtmoZj7Ji67YXuocs4boyun6EWZyjXLvivga1H1U2TqI4Z1u7YglXrbOuu6CajCaG5VklTVk1C7gV0c8gpAuaLkt4LazchNqtg9pN7ZZ1xtVud3WR8z7Q/dcZ243MBt9moa7gknrRvsypPMQtWdUxXV0AYPiTx5Xtl1C7qd0x66Z2NwZqdzDUbmq3rFPVbsN0vMmk17bive16cauJIIVwxzd1jO5TwrqogdralfLesV6Ou3IFum6M9K5a90HtTlaW2u2D2p2Oqk2ix3UhCyMqI7ivvZhxylTXp2oTx9WsUlRR1+2vRZuSsM9CdXsrczdTY7nFiKOnc8OLeuAqolz4VVczXZbzsLZ0bmyyn7r7Tn04CapFCLt0TZR7SMZCd+t0JrfV6+nd5uJowIByrJpxXE6Cu3V7JtGHzkXvTiRsARhFwMwDyPl3ZtaXu1BJ5ARC22qlvtKQZDhZwuV7l0J3fJcvlSD3d0XEy9qNI9rqfvm/zi1frUO6jCmuY94JENmvrHNPqBncVRHv0TyIFYSIFEE3W3gMdy6dSFcitDoRV4njblYLwU+SlbwVoHZTu6ndQyTRbhvB44EaG73D6UNR/sg2C+VlHeQ+ISfNpd7JH7E6F23vjz+Jzk1abqN2U7uV99Tu1oHaTe2mdg+RRLtVdL97M5Z/3IbnvNQxs0Pprxq2RTfuZQxDOyEqKah1ULup3cp7anf9qUtiUUJIMP2dnXh2px0w0J4D8gErw5qc/s52/OijO2HVqPbowoQQQsgwoL+zE39ZuCPWt6j2rRjTiR8dRu0mhBAycujv7MS6Qz4Ee1QHso3uTAqKE7qp3YQ0ARVPoiexhLvZmlO4l8V1XaoGaazcav+i3LhqSVC/q9l+lIU+LqZpwTRlyJKoZCjBtuSgVdkqQ8lbgi3jOgzTjExMk/S+s4tFN2EoAAjLwmBPT+mNm0jUcUVzrrP87NxV5I7bWQHFsizu8nq4qw4UQ7S6qryoWN9FyKpzeaxcxS4TriyfkEXpKjtXOsjdS7E4S1RLPUz5GZm+99LVqGitQd6bhdz7qlqilbZVi7OR2eBmSZdIi3PeKDrt9ZWqFH6Xe102bGFn9P3SWcTl+TgrD1XXsVJ7lq9dmbm9zH1M6V+QlVYNMSAfw+Q55NQ04QoZX7+CE6yoSXK05WNYkXUJVhptgVZJkk3dW65RULv9x0io3dFQu0va3TuhGwaAjLMCTn6nLUXXCsr7fsRYWePoQF7qlm6Vue69B3ell6dMoQ1Y3t4Gaje129s+tZvaLaF2x4fa7afZtdseXxrjvV7YvlfL+b0rV6Q7lyJwXFbGD92KdPe98xoYlsVBtzI4LwSQM/m7G9Ru973zSu2uv3Y3xUp01fUmifBUU8SrSVLxrEU29DT90OEV8MpFXD7UFd0HvAxKA1whHxFvS1eXR9TVbZKyeG8R8d0sy4p9f2ld6Jw6vQ8S3gfgtr5+TH75Zbyz6VQMOse43wfnoSw/6Ay/WXn80GS2zkVvaLLc6U/RGWDUB4XQmIWawch5FjEtA7mBQRz82Dt4ZO5krOkpuZV5Byl1gLMQPMh3O+esG5CkAPXbNgpGqc/rzdUAPDFkVbFUYsca7e8BgCvgHaalHXil25YaD08n4vLxrGgVUYjql0bMpYjLTO4+0ZSvUsyV6xXlzhUmMEPugcHnVuZmGOBuqLbrZpjXHauUD/qWxRX+pNtrTZps6q0KtZvaPZK1e9f7X8Vzu22K9V0lcZa60e+MAQMIxgLKxlvfPgydszsB7uhE1ioGlg+j7EcqgNHrBrD7ky9Tu5331O4S1O6RAbWb2j2Stbv9/jfQv/MM2Bspk+m2MqGsEKaRahld7PRYk5ia7f1CAKs34ODHnqd2O++p3SWo3fWnNgHLCCGxsAp5TFz2HjKFQnThJiRTKGLuv1agY6B+Kz4IIYSQRmIV8tj8uXeR629N7e7oL1C7CSGEjCisQh5tz/8HxkDrhVAFAKMvT+0mpAloyEr0KPcy6RZloTxDtrTw1tMSLvsQlkhEtRLHTToij0uTeVxHGit4XCu3ZZmVW8RlUpCAVdAym7ZKVCb3NPeDLjt4rGM1bm46a3wQtm37kpnIrOaZbGlVW0GJkV6UbmXOivRC3nZDu7h1WMFWwEK+qLx3wr4oq829x+tWoqtlLMtADkOWZp+1W2PVVF9Vy6mOdsNwV/nJsn2KCxjguII5yVOGXLFK19ebHERaL/uV/nnbC0JOm7hWY4/l3GsdBwCo/VIspvK6SUv4GOmRENC2ztLcrrkf1QHee3661RHZKLcyT11qH3V16l6TfGsrsXw30uWq2dzfKoHaXX4ctdvZNwK1W46Bchx0x0alfNBKIBmLNR+xgsYdKxV3Y93qLF17TsfdtnNOv6nd1O6wOqndwwNqd/lx1G5n3wjUbve96p2tvEqhdscCrwZEjA+6VcRxxhVDeSwQziHtTrvUbmp3VJ3U7jq0U5dWYuK6l0Vkb24UcURMjVumE/UgEZfUIvN4Nd3W0sZmU12tMtksbOm+44yAcoJYPujZKR7eTDNYpG3V9Ssi3ltw3f5jovolz9XNMl4suv0zzSJM04BhGDBMy33IlQ808hgrU/qayvtioH/onslmHbe8rNovw2lfyTjuPDzISfSist+yDXdiXs1WrtbtHoPSgJXEZS3INclXZ9CDAUoTDlI42xUh69d8bmpG7Zz3M3SOkfHJ4royue5m8r1TriBEWayzKGT/upXXoLZ1oqjGmJOExUbTPdjprkGZEHs+c12dcYmbGTwOjRK14fSDOynUbvi2UbtHhnZLZAzUohIb3dUa2S9PH6ReuXFHlfbV3mUVvQubRI8iLwQKQlC7Qe3W1RkXandrQ+2Gbxu1e2RotxACti1guGFclNei/7eyDIlqWUO/kQsIDzuiTrrKiXF1ot701OmegwzbagaPTdRuareuzrhQu6vQftoDkyQ2KTu2BnHI7JgPALZtawfxWqCKehwB1CVH0Yl7LYQ6SVm1fVW0o+KWebfpUO83VbCH6jHL/lfvt0pWU7jW/JgPWkFWf/cBxS7FfjNM04kpJ+sMdjETtpwYl9fR0E50qw8AspycNBfKg4Knx/pzccTcfbUMmJaBAoABIVxLteW8qslVgPgWeTVhh0tAeVeINclc1CQgXooBAhVUVrdaMOMRcbV/UWIuHwRUEfdat7UC67yq/dUKsfI+qEzcVQnyvLzW8Chrtc4SXomIN1o8Jc3Sj7hQu+NB7aZ2q6jabcKvUZYyzsnxTeqM94eebtyIimepW6UVlqxUB7Wb2k3tbh2o3fGgdlO7VVTtdrfrVqI7dRQGZR+c3722gbacEktcGavVVcWyzrziAe7WqfEg91I2gQxqN7Wb2t1ommolOiEjjUK2Dcs2m4pCWza6cBOSz2Tw2A6T0NvRmv0nhBBCklLItuGVbadioEW1r7cji8d2pHYTQggZORSybeifOx12R1tEEI0mpauN2k1IE9D0k+hRlmTVEh43JhZQ7iIUFf8rDrp+qtullVvtv2lZ2thq6nad5TwMnVU9riXcMMv7535GSj+iLOPVxA2FEnB+cptth5+ju8LbLe9xAdNcH2/Z4P0BboPZNre9Ymcn/rP5jNKOwUFfna6bpWwj4N5SQ6+ormiDSuIRud0wTOe1dLwQ0s1RANI6rgnr4n3fn8ngLx+Y6tsvW1xv21rLpxsLTX2NsOB6rcxqXLCc8n0uc3lS6iwI4VqhoexTkcdKK3ZHiLuW7GOfdNMPsCB7t7tuZc7n7V1pGBSfzUuH4oKmu25hlnC5CEIXJiCsbp0bmVpXlCU8jgtiFGnELI01u1ks4M0Otbv8GAm1e4hW1u5/7elodwXXRY6F8uew6gYdx00bADJClMcMDTlmXXcbtRvUbmo3UaF2lx8joXYP0cra3bvrTADJx56g8CrqCnTVA7wszrqCbYry/GZm8DhbEAIY1UHtBrWb2t14Ep9/Je5kSQkSDh1JBCKuW1maGGlRx+jOJ6j/UQ8VugeCIGRd1Yj7piabkcQV9TSUx1szA/cniuFW9rDhF2bTMmPXqzvHDErCXcgPutuMoqf+QgGd63uxoacbtmwjH54xXLp+GQFirnMbk3HVLe033vnsCoWhX/NyjyYmm2kZsApFTPvPOrwzoRODbaX+ewVXip+8Ku0IJsotyX0vhOu6lVfEMGow07k4xemHjPsWJ46ZO4Eh3QF17mXSnUuZNPEKeJToRbmEqYIdRIfhF15duACVIJcw3QOBjjgPHVH76iHizSrgSaB2V3YMtTsdw1m7N357DdZM7EZ/Nt59KSmgXBfKwrbInChykl0Zg1RX54xh+JOeBbThpW2wiI1WbKB2e7ZTu6ndzQi1u7JjqN3pGM7abb71PooTemBbOacO/0K0oTYM36uvHXWyedDRDeX3t/574sSmhz30u14z4Pu0aCDP393K8dRuancjqF+QMkJIGbkNfdjiqaeR6+uLLtyEtA8M4vg7X8S4Nf3RhQkhhJBhQG5DH/b8/T/R1aLaN25NP7WbEELIiCK3oQ+j7/wHzDWt+bsbKzdQuwlpAhIbE6LcaBpFmYtYRCboJIRZkeO6gLn90rjBpbEWx1kF4LpyRbjM6dzhVHezuCsUAL3lvBqfSZilWld/1HY1AUucY+PitYzLMC0i2wYrm4VpWchksshks24Zbz/cV3cFhO17BeDmIlUThsoV6OqKAt331zBM14qeyUpXPcP3PvJcA1ys3G6qVmzV7SiOBVhxVVdf3QQsEeUCLeK6c1LuibCB063f8q8Q0PWrXXNvZQ1Db3nXtalYwNUEN0HH6D4LidrfIFe3qBUNqjtZEku4jjjildaS3QoW8KRQu4P3xd1O7aZ2q9ptGUCbYbiriPqVMVWupFLHzPZSx5R2nFdlnE2SbEv2oyD747Tfp9xvRQytUlPrkFC7qd0AtbsZoHYH74u7ndpN7Va12y4KFPI2LGUFuvztLH8HD9U11D+5T64eV4+JWoFeHmLHHEo26myRK9LVhKLac6V2+46hdlO760HqmOhpRD0qO3hREbQkwqGSZvDVCV0cF7GgGGte5H51UC0T9wSiHtWmt70yUdfUqZ6rmuU8DkkyouvcsdQJ5KhzDfq8dZ+Bti8h90zcuqLuO9O23MzhRrEIwzBhGAYMz3HyuyUn1SWyjJDuSvlBQMj++B9EdGFbbGUi3m1TxrazTHeffCCQmcjL6ioK2EWBjAXkDKMszllBCKiBadRYYvJHv5rJu0x4neMzluVODOjEOio7tyyXCxiwdVmrdRMXoSj9Kyj9U88tKON32flrRDDnGDqkeMdxz9K5pKlirb7PKseF1a2LxaaWDyPqQSZNnbU4tlWgdlO7dVC7Q9oI0G4LBrIoH/ektsh4m6qLcQFALkJTdJPnReU4yzNuq5P3GUVTZB2W85cBtTsQaje1uwmhdlO7dVC7Q9oI0G4hBIQtPAYVJ7SKMkE+1LZwt+vai5o0Lw8JM1TPYH/p3DJtpW1tmvvfG8+c2h0AtZvaXUcYzoUQQgghhBBCCCGEEEII0ZB6Jbqk3m5mqqtS3OQZXotmXKtzJUlAdG0ksYDHsXhXG10m8jSWcbUOeaT3vHTubqpFV3fuYRbotAlVKrnOSTOgC9NAPtcOGIbbbqatzd8fef1MaSkv2Zm9q9eFpt2418Ab7kX+L9vJK5nGvWQGbfSNz6EtYwW6R+mSp2kTcjivatISb91yW79iWYZSVtd2TrE8B5Ups4xGfK5B1nh19Z9qIddZmn31avql9k+1hMdx24qyhKv9CrLO6/oV142smhboVrZmNwJqdzDU7vI6qN3lCNPAYE/Ol2zNXSWkhmpR9KHftstWFKl6FeW+XbaCCkOrrtw6nHOSK+K9bXRmM+jroXZTu6ndrQa1Oxhqd3kd1O5yhGmg0JGDLQAo4VzkinBJUHhT09KtOPePY7rV7Gq/7aLwhIYxfO1aln9cNk0DME1qN6jd1O7GU/EkuqQSUVfdyeKgE/U4RJWNEvEwd7K4g7mcoIyD6hLmbldcr2qBGpcuyFUsrsCrou4lKnacWy6hWMapQxuzLeQBsBqZzy3LQmHMGLy07zwAQMaTSdzbr6HP2f8+qH011l/RqaOo3G9qlvWh7Zbns5DHFpxXtayBtdk2XHf8TqX+O9t9g2oMUQGADinySsZs1x0+4DjLEUdVNGU/VDeuMDco9b0u9lkUQeWSurtJ2sPE0imqE/E4Yp5WxIMygScV8TRUTay8dY7wBwCA2u2+p3ZroXb7kdr9+Cc+CADIiaHYpgCQUX64ZZ1+5z1apJskV92m+xXd0I3XAMpclMs0z/O+b2I3tTuiHLU7vB9xoXbXBmq3857arYXa7Udq93t7lbQ768YzL/r6JSfV5eS5N5zL0DkET5qr+cPUU/VOnnvbAoCCkq3EtAPGuTFd1O6IctTu8H7EhdodDsO5EEIIIYQQQgghhBBCCCEaKjYyxLGA6xKbRFnCDdNyLbKq1VVaZitx/QrL/p2UuNm+3UQnCVzG4mY8j5PUpRbW8ziJTILw9qU8W3X6uiRqUhl3ZXeEBTwwS7gmwYlqGY/KOG6aNkxz6B7IrV2HmU8sweu7zMWG9nYAQMbJJ2or96XbP+/qEyX5qPyuye9l2coL563ue+u9L90QL7okpUUbXX39OP2G/8MdC2bh/Qmdvv2WYWiTo0myiiVcJkkpy1ztOUaWkSv51IQh8r1qIQ9DZwlX90dZtUPrknVEHe+8tptmYottXAt/GGFuZEGvgN4Sruu/zp0wDWms2sPJEp4WancJandyqN0l7Z7/s7/hH4duB0zocgo5rt/CGf8UDZKrysN0RC2rujTrkm15dcXVXc31KgqB8Ss24OC7X6R2x6mL2q3vZ4L+RdVVq2OGG9TuEtTu5FC7S9o94XeLsXK/7VDceJSvrGU5K86DVoADKMvaiaEV6TIpqMRdxa5LOFqWaHQId5V6MUC33lvL391x66J26/uZoH9RddXqmGanFiv1q4oqtDpxjyJIcCoVcbtYjC0+uizh6n4vQ5OmZvD2CGGuRLir8YATVbewi7H7qHM7CxNxt0wMt7HA/Z7j4saKi8K0izBtx1UKQCZjoT1fQFsmgwE5IS4nuouaB4KQhw3blJPn8hzk90Ve8+QPS6bmXjBNC5l8AZ0b8ugwjDL3pA6PW5SkXRHFcnep+CLguo8pr26dKTJ7u3Uo74PaD+xfSFn1ve7BwHtNQp6xysoGbte0HYT64KUTcS9RbmRqf8q2R/aqusI7HEW8WaF2U7uBYa7dvYNox5DOZZ3Jcxhysrz0NuhHVeRYJMO6qHqWYAwLivcpt3cYBsZQu8v6FFSW2h2wPbJX1O5WhdpN7QaGt3abvf0QRRvClpPmpVf1jrBNR8shjQJD45AMw6KGdSlrPyJWetBkuhryxVvWKBSp3d79IWWp3QHbI3tF7Y4Lw7kQQgghhBBCCCGEEEIIIRoqXomeJrGJdCeTq2NV9zIrwNooy8S11KrZrsMs6Gndorwkyf4dhTYjdkSm7KD9tbRsxyWOS6D6eUW5Z6nlvOiufdzrGnY9dUT1N6hPBZS+P4ZhwAg4D5nYU/1uha1EL8+27qwkcPplyJUFIf3SoXWtQ2kgCcu6rXsvkZZy3cq5OHX2q6tNNC5Vqjua+r/3WO1+pe0gy3O5td9/bA7h55qFgTyC281CYwGPsIQHufqpaK3r6vlA/3lVw/I8nK3XzQC1uwS1Ww+1O1y7pfapY6Mu5IqrRYYR6cKsrg5zx0MZEibknLTtevoPULu9bVC7g+tIA7W7tlC7S1C79VC7w7Vb2KVV3kKGTXFWnFsRq8q9SUDVkCtyRbmt1KmuWNeVD6zDLtcRoyio3aB2R9WRBmp3MhoSzkUV6yDxjjomTlw3L6pYVIu4oh03dltQvDKdK5XOXSpIwHUPM7oHmbDyOnGOOjYO1Ygdl9TdTue2l6ZfUdnDzWLRfQAuxWkbqlcXckXGSFd/NJumqe2XbD/jhIix1QdoWYdSPuw85US/8DywmJ7POsyFSSc2Et2EQZhI6tpQ+1OGxy1eirQuvptEfa/2r91zD5ULaOlVzegd1f+CEJBaLkU9Kiu4er1UV79snOsYsT8O6vUkwwNqtx5q98jVbklZWBdXYJVyCBkb1TAuOpfdBG7TQcf6XKB15QLap3ZH95/aTZoNarceavfI024hBGwBN5yLO1mdl9dV9k8uRCsfD9XJ8ELecNpwJsKtkLjmal0Bk+W6coZH06jdwf2gdpN60PQx0QkZzuS7OvHGhz6AfGdndOEmpD+Xw72HbIM1o3ON7gohhBBSF/JdnXj+yB3RN6aj0V1JxZrROdxB7SaEEDKCyHd1YvlH5qDQ047KzQf1R4zponYT0gS07CS6ziVNR5gLk7TgqpZdnRU9LFmJzhKaKGxGVBZrjaVctYQHWaa9yUV0ZcIIKq+7flF1eMvr3N+ielfmzhcj4UycVddhfQLK7w31uuos46ZluaFVTLsIWDn0b7xR6X2+lA1UfimL8nO2lX7IBKQByGSkGaWM3F52feT3yHPPqd8tufJcvsoV6ZZlAdksVm06ChaGkl2EWcRVpMW2Q8kKngSd9TXQwqx5rztWl4hFtYTrXMcAvRU7qm1v+6rLnHZ1ooYwVzJVBJJ8BjJsQZSrWhILebWs6LTGNyfUbmo3MDy0u3ezsTAAtMtx2inr6oIcHx23bp2bMADIxWryWOl5JsfYQkyN8m7T0W6aQHsbVm3aRu2mdoeWo3YTCbWb2g0MD+3eMH50qY6iPnFoqW7DKWd4tgmn/dK2YsRKc1mnXM2uhnXxoq5uD9yfsbBq0zHUbmp3aDlqd+1p2Un0eqAO0GGkERVtXXKw14i6ShIRV9/Lc4vrhmeYlnZfNR4M1AeCqH6pommaZmQ8N/fYmNc3CJ3Q60RdYheLQw9jpgWrrw/jX34ZazafgX458e28FpxJdXluajgV7z6VDNrK2vVSyA/6y3sm3dV7V21BPkibloW2wUHs/tc38a85k7ChO+e07WmnrF9OHdLtPaZrk1cMoiYIoggSce1AqHHXkiIelcXbS5QAtwfsd7OWO9dc++CitCHLqW5llfRPpQjEXsUR1+UvjQCPNNEm0VC7g9uidldXu7f639ewbKdN0Cu1T3FT7lfdlgO0y9UjQ+4rjWfdmnMshsREj+0ObRjoXD+A7f75LrU7BtRuz/uY5dLUTQi1O7gtand1tXv0U69h3ZaTke9pBwBY6gS3EsdcTmr7JtmVCe/8oP86lh0bEBJGrdMtq4mNbhcFjHX92H0Jf3fHgdrteR+zXJq6RyrpRzNCSMVkBgYw7qWXYQ0MNLorqcjmC9j+ybfRviHf6K4QQgghdSEzMIBN/vY6si2qfR0b8tRuQgghI4rMwABGPf8mzP7W1D5jwyC1m5AmoClWopuKZdSuQtZulTCrdpQ7VJhlPMzFLA5qdnXbHkqAoZIm+UdUlvAoy27YPvV6yOuoS4ASx2Ie5aKmazvNtUlyTFTfdZb8oH563doM04JhGDBNqywEi7wP5D0ij5Mr1IvFIqyA1eneY1XkObsJR9X7r2iXrVJXj/W6NVqZDCwAbYYRuHIur1iv42QBBzyWdHXVXYgrWFkdMSznQVbooDpUa3F7SNtRyUhU1P1BVmw5UJetMIiosxhy/VUretzEK7WAlvDWhNrtf0/tHlnanTEM5AzDdYuWepFXVp4XFB0rCFG+StzRiwKU0DBynFNWRanHq1rrqztAt3KGQe1WoHYnh9rdmlC7/e+p3SNLu21boFgooph3wpYWS20ZzjJndWW6NxSLLtSK7K1ckW6ayu9yzXGZrBm5Ar2Qt93tZsGmditQu5ND7a6chkyiq+IdZ79O4JPGaPMSN5aYUCYx42b8rjeq4EQJea2Ie13DiBRP5b1XNKuRaTyqH1Ex7sKugZoJvjQZnUUmWwrBMuQiJyfR/TFSM5563HtQ/sB29g09MDixw5zwLrqYcfKeLgwOTaDLyXQ1FqCcgDdNC235ArIoTSIUAsQ8yH0d0ItkWbmg95o6o2KxlZUPOVY+6+Tg395hRD+QZpRjImOxaerJGoYrvmETJGEEue9FiWASkXTvIk3YgLK6Y9eshyLeOKjdtYHaXaLltBulMdYd1xQ9UyfVISd9DKOsjKpXlvOjUI2vrtO1fiEA575W+6PqQMYw0GVZyBrUbn+91G5q9/CE2l0bqN0lWk27hRCwbeGJS277XsIm1dXY5mVtOa+FfLCxRw1pYxcF2trlb3i7bJ/v1Raw8ja1u6xeaje1u/4wnAshhBBCCCGEEEIIIYQQoqEuK9GjLODVwFKsoGks5HESmXixi8WKreM6F7KRQJDFPu5noLqf1cL677VyJ3W/k1vl2Wi/AR3tWD9zc6CjXdsPeY9knGgv0kpsWhZsxwot70P5hS5K67QSMs39LiqhY+SKE6+FXJZV71GvRd/O5bB0y0kodmSDBxONlVpnCdcNSIWg/5W6dCv64qBLHKJzDVPbTIIuK3fQakG1TNnKxghUN74ga7LuHKJc7bxlVMs4rdbDA2q3Hmq3n5Go3St32ARGR3ZovFPHUnUc9KwqH0ooGjxWZoWz3fAnq1JXaQ0tpLPdVWnaRFie98WOLJZuR+1OArWbtArUbj3Ubj8jUbvXTtsIBctyw6Wo2K5Xl+Wr01/GSSzqrEzPZP0tylXlRlEdU2WonfKxVg35MrRS3nlvC9iWRe1OCLWb1IKmiIleD8Liq6nbdMKsCrdt257/lRhcyiAv9+smJJvNRU2XPTwJUS5ZSfcFtpGodPVQHx4lUQ+RZVnDR43Cil12Lu2LeQ+ocdZiHWMH91e970zbQqZNCSsT4qZX6OrC/314S+3+sgEmYpBX3chilVXEqMwVLczlKcIdKmncMu/70HahF3FfGXVDFUSymoO+7iFIt52EZ26XsfSSZHcf7lC7k0Htjmi3itr9zrxtnDfBmqyGVQlEM5muhnHR0ef8IM8YhhsztCB1K+S4wVHt1G7Ne2o3tTsIancyqN3JoHZHtFtF7X53p5mlfcVgfZWhWuT9ZzpT8mEtyUnzoDjqpbqE772Q+y0DBWfBmzzG7avSP9MyYI/qoHZr3lO7qd1B1Eq7q/a5qkkQG01YzDZpRVVjrqmocaxc4fa86s43anvY6l53mxKLOqxsXJJa/ZMQFU+tGlZr9XMNe0irjZU8Xp2ybdUyDvit40ahgOz69Sh0dblxzSW6pDlyhUlQvEI11lrZw4Nyr6sr1k3Tdle8q6vc1boBwCgWMfb9Ddgwuh0DGedHf4i1WJeoQ6KzznrFocwCHlGHLslKxjCGYtgqx1pKG1oLfthkhcbaHyXiYXWqdUh010Ceu7cNNba+SiVCLO8u6eeg1qVreziLfxJhbsQPcGq3eiy1W4XaXULV7vbl6zEwpgOZTPD1C1sxVfZdV8dAZcxU65Lv5Yr1ojE08R5npZaVL6JzTT+1O6jT1G5qN6jdSaF2B0Ptrk77USTR7syqXuQ7c0CH3ytbh3cyXdarriRX45e7v6tl0lBNDPX8oA0nJZrS04B+FAWQL2JsP393U7tLULvLqZd2MyY6IQ0ku3YdNrn3PmTXrWt0V1LRvmEDPvzrp9C9qq/RXSGEEELqQnbtOsz65V/R/n5vo7uSiu5VfdRuQgghI4rs2nXY7IH/Q9v61tQ+a3UvtZuQJiD2SvS47k+maTWNVTwKNeu3bn/c8v6yjtVSsRLrYrEFrfKV11y3mlhul9bgIetoettIEitymDU6aV1xLc1qedUyXkkbYS5hcfun65drGS/a7jYTgGGZMAwDZkD2aVNZCT60Pbov7r2jrFRRV5cHYdtOGefYQn4wsG71/8C4X3Kfpq0oC7jEa9WOWmUnyxZjlOuQ37uYbmPqdp2FGoC7sjCJq1zc9svKOa9RcXaTEOUy5t2nls2rlvAElvHhYhVvFrduaje1W1eW2h3drzDtNg0j1vc8KKyLulJLXSnlbld0RNUTufocto28IY/Rr0pT21P/l1C7qd1B+6nd9YPaTe3WlaV2R/crTLslakx0uWpct93fd+F71fZPCetSFkM9b0M47WWyQtuexLDLx3Ev1G5qd9B+anf1qUlMdJ1g6Vyq1P21Ric+OvEOo2ySMiDERq1xBSOGiOsEuBYuWJKkQt2oumtRV5CoFyPuEfXB2XUvDJgAH7r/kj3AuW0EuC7K+OkFlCbPM3BipAe4YaaNKZgmMQgQnNhDdcsqRoloQPwrVWx0iU3albZ0Ex++Msp7XdlYDwi6NjQubLqJlyToEsdkDCPSHSxp8pThQLP8AE8Dtbu+ULubt64k2h17PAtxj47sl9QHdVLdOxnu7JNx0oHgNgpCpNZgandAOWr3sIDaXTuo3dTuetWV5ne3RI1B7m5345mLsrjl6m9z1Tij3tFlE/SmgYwTGELGVc8ogSLCJtXjQu0OKEftHhY0SrsZzoUQQgghhBBCCCGEEEII0VCTleg6osJQ6DJrA+HJFOPgul6FHB9lAVezhKdBXRWgrjYGKktgAsSzblfTAq5a2Supu9LPud5tqW6DaeoSmkQ2kW2bXpc0zSoT5V6NOmfLslBwrelWaFlJwTRQDFjZFmSFdd2JIqyGulV6QRZxdZuaiC2O9VW1fIdl//a+D0pSknSFYWhCkwhLc1QWcxfPKgG1naTWbO/+qHZV97IohrOlXCUoK7wk6D5vJNRuancUI1K7rZjjr4NXGyKTfcXuRGmsaDdNrHfuSbmiC4ZczQS3XV8b1G5qtwZqtx5qdzyo3cnLJG2P2p2uLtswULT1K8+1xxWDVqILpYwM/eOEb5GrzJ22DHdVubPqPGsOrXR39skQMWryUtM0YZoGtRvUbh3Ubj3V1u7Ek+hxY7Q1mrA4WzqqcW7q4F62X+tSN+QGpMZei0LnThbkJpXmusRFFXG1/ahrE1S2FqJeFi8vRVtJziWM/LhxeOuYo0p1qhPeupjonuuq3ivRD6SyfHD/TduGIeso+suqbp+2baO3qwv3Hb9HaJtAdIy2yOM8A5860Kkx2LSuTM6rV4BV8VA95uKKuC++rObYuCR5+NBtD7sGUfVHiXiS8woTrFanlu5jtbhu1O6oOqjduvap3eXkx43D0i/u59um04sgl1yJzm1X/gCLG2fT++Mqzg+x9yd24ebPU7up3cFQu9NB7U4GtbsyqN3JyY8bh38f/IHSG2fyuui8Wqb/+ysn2b3hVIZCuxjaMn78YV6EnLj31ulss83gfngNPsWJo6jdoHbroHanI811YzgXQgghhBBCCCGEEEIIIURD6nAuzWoZVy2+aiIPyzLdJA/SgqBb6VsWCiMgk/eQW5g/0YmO6CQvequsaiFXLdC1zpSdtu5qWY+rhS6De5o6KiWzZg0mLP4rVnzogxjs7g4sE3TfVUrYfap+jvZAsCskALT3bsCHf/Mv/G3/rbBufKe2Tm2WcGV/u3Ou0irb7mzvD7DSRiYyUeoOs2LHRfZLm6QkcY2eY0Nc2aIs3VFuZnFc7JImnUmTgEXnQjeS3MkaDbUbvjLUbmp3GjJr1mDmdX/DWwdtiw0TgrXbLVvJ+KbonKxLXRmWwZB+ujj3ZNFp3jvG97y/ATv/6SVqt9JWGqjdpB5Qu+ErQ+2mdqchs2YNNnvwGSzbeQsMjvJrX1FJ+mkpq80rIzjxqLdud5W7Xb5aHSitdjdXrseH73qa2q20lQZqN6mEimOi11vUk7oAqQIXlCE8apJSJ+pe4U2a4TxM1GsxaVpL4op4IzKoh1HL/kQ9VEqMYhHZ91cCg/lU7ZRlBVe+j8njrJf3U2TbAAAFDJbKeOKvWxAYv3w9ckUbfc62IFd1HaoLVlRW6bD6ygRN9lEj4hnDiHQBSxLfDQiOfaYSu64E7vlRVEPEg+pQRVreGZU8MCVpvxE0Kgt4taF2O2Wo3WVQu4cI0+72d9fBKFTph33EuOLGUHfG6w7nXuvzTgBpftRZKNexNpva7SsPajdA7W4FqN1OGWp3GdTuIUK1e00vDM+EuRo+RZ1MD0IN46IL8xJwpNPf8vFIhnORr0U33rpz79oGrP4CtdtbHtRugNrdCFpLNQghhBBCCCGEEEIIIYSQOlLxSnRJLSzjpmlpLc06y7i0zsZxj5LW8qSWhKDM3u4+XX81Ll/yuoVlCVfLVJpFPIg07mSt4kbWTBSLttY6XubeqLp8RewH4n//dPdQBm0o5AcD65L3cCbrtGVayGTakMkbyJkmup06pfdZUcS3tpZlGFdcxoLqcS2TSgK2nGIBD21TsThnhPNeWbmXxDKuS+ZSiRubSqVJSuIQ5uam60MtLOGktlC7nX3Ubmp3CEHaLQB4tyRdQeUl7Zjt1Tmde3HW0TX5hckIA+2miYxB7fbV67xSu0krQO129lG7qd0hhP3uVlegR233ok8kGs7QCnXZp6HPVK5mV8O6eHtv2oLardbrvFK7ST2p2iR6rYiKeRYl6hIpVoZpucNV0TkmrqirITKCHlx0ol3e72CBDhJqtUycGGy1JKmIN5s7WRTVfLAZqrP8GtjFIkTItQmL06fuV+/FsveaugLvNydci+kcY8i6lDiEpeNNWAbQZhgoKgN5AUNiqZKHIjZO96JEqN00kYnICq5OILgPBsprkODIfXm5y7kGbsw4WXccsXTKtscQwaDziLMvjWjGdeFT3wdtV++esLJpadYHAzU7fVJ3s7wQwzqLOrWb2l1vGqXdUboVpFFxj9GO0/IfzxhS1Izt7hjqbM4aQA6gduugdlfUz2ocW0uo3eFQu6nd9abe2m0YBiwzOrZ3HExlwl03ua4L92KaYihngBLORb7KT7lgCyBvU7t1ULsr6mc1jq0lzabdTT+JTshwptDVieW7fwiFLn1ykGZmsKMdz+0zG32jc43uCiGEEFIXCl2deOMT2yM/uqPRXUlF3+h2PLWA2k0IIWTkUOjqxLJdt0S+sz26cBNS7KF2E9IMVH0S3bSsmiQ7SWsZl3jdzaRFWWcZV91+XIt0QGITFd2561zBgizhcROcWFVw30pq/Y2y/AaRNClNvakkM3pUHToXMomdy2HDZps6byJWnsfoZ9wV6HGQ96H8zIUaTsaygGwbVs/aGBkEW0HVbUPuW37rdEB+lVI5+U+ApTUyeYpTtt/pd39o6XBUa7qsSz6CSeuoZRhlVs64LmlBluqorOBhx4aVS9IfnZXbQrQbWdk9Edpia1OphbzRULup3SrUbj12Loc1W2/sFA4fd6Pexy0ThJtwFENjTiaG5ogOajdA7aZ2U7u19VK7y6B2V4dGa3fv1Am+bWlCs0StQHdXnCtJSstWpNu29jNWw7s4DWH1FtRuaje1u9HaXZNrW+/M4f62k2emlqKY1M2sVL8+Tpt3v9u/CDeyoHqiYrGpbmZpBDeKWtQZhBTFStqrVJy9Aqx7wFP3p8Xs60PX62+gd9pmsHN+q3KaSfNq4D44K26URkBbmYEBbP7U21i29UYY7GorbQsRDFVgJVIUpTtXDv7jsp730iVNLRNU1ttmuxRip+2wjN4yZF1WaUIV5jguaq5rXcyHj0BRV8uG1lSZiOv6o97p2YCHNa1bWawWWwNVtKPKtdIPcmp3+X63f9TuUEaidk9Y8jpWz56EgqN9kiSxMaPce9PE2XTHYaUN77jc1juISf96j9pN7Q48Jm5/Wwlqd63apnZTu1tLu8e8/B+smzoBxfa28LIVxEZXJ8/jEDhprrRhbhjE5k+9S+2mdgceE7e/rUSzajczURDSQDJ9/Rj39P8h01eJvbZxZPoHMOvhV5BbP9DorhBCCCF1IdPXj8l/+TcyLap9ufXUbkIIISOLTF8/JvzzdWT6BxvdlVSYfdRuQpqBmhoqapU5HNC7l0VhWZYv2QkwZO1U3bR0LXhLZdpKVkBd8hPVwh3HEq7uK7d4m5rtya2zlVieVXcx+ZpmVYJKXKt2NSz1Ya5fun1y9URU60GJTSql1itN1O+W2p4M62JEuD/6rLAh+7y0G+F1FoRABtHWaC/dGuu1apWPQz2TbSRxoQs7Nuh9nH267UGuZGkt4c2avKRe5J3PVXVFzKdYfVpNqN3Ubu/2JFC7o1eRp1ldriMoMVql9VO7K4PaPfyhdseH2u2H2t282i0JW4GeJvRLWN2++zDGyvcwqN2VQe0e/lRTu+uy2r8ZRd2LKopacU/gdhblCqYT+aB+qf2IEnGvmFYrHpouA7h3nyrq9aAabmjJ2rND30cR9uCgxv6rh3umrFt+j7yfneyHvMZqTHRh2xBiaFvQgK4bYOK6I0m8QhxXxHVtZlx3H2DADna/ks9Irkua89m479U4ZVUQpSCXtUpFXN0eVp/u2DTfrLjCkvazbCXiuJc1+oe3Dmp3wH5qd1UYTtpdSegVHXHjb3qRY42cWNfF6oyjv9Tu+FC7hyfUbqVOarcLtbu1tVsIAW81thNLRE6Qq3HLJZVOoOvqspz/5TbZblR71O7KoHYPT+ql3QznQgghhBBCCCGEEEIIIYRoGE5x56tGVFKLIOJm9i47TiZtDEiUkdR9LMhqXWmG7jBLeLXbAsrPLa57mdcy7s0IXy3SuoeFrjy3LKA9h76pU4H2oaSiUdnog1aYhGWuj8NQMtH49RimCZFtw/szJwC5rLvdZ71WjtG5l0W6nXmtxMq+sGQe3rrLVufZQNYMT+omLeHtisVetW6qbkFhxLVye8up5xCXoJUHca3QcSzhUSsZSLLVEvI+atbVbc0OtTu8zqiy1O4h4mj3+i0mwMgNqZcu8VXYmJ9GD+Iijwk6ttCWoXaD2k3t1kPtrh/U7vA6o8pSu4eIpd2TxsLOWCg6q6ItZ+V31Ip0uyjKVodHhWDRrWr3UnTazWSD97vt2wIiS+0GqN3Ubj310u66TqI3Int4NQRGEpRNXEpd0nPSuZ15Xciqmf1bvQ5R1yWJiDcraR8M4tXtvxfiYpiWT+ALPT1YMW8vACi7l9QHRDf0SsDkuRqWRWIGPCSGYVpmIlfNwa5OPH/o9gDiDSZSjHTiLQe+0FhVEeKt3a5k7W6P8QAeV6SCYmyVDci6firv+2v8Iyyt8MZ5YKFVtpw4bmVJHgYbAbXb2y9qd61pFe1++4AdS3WWFY4f3kX9gVaJq607xsRot29sB7Ub1G637lQ1D2+o3WnbpHaX2qN2N6t2r/jg1r4y6mR6HKJCrtjuxHj8z9WdxFf6ISfi7aKA3dVO7Qa12607Vc3Dm3pp94i59l7xqpbFNInFXEeQJTypiKexWqvvqxlPrboPUNWLuaYT9zgrINT4eOr2JKLua69YhJnPw85mASX5TSUPvWmvfZzjZL8s04Rh28huGEQhl4FQrlHY4KIKik7EJd7tsmwxQvTUwVO+l7HZAhOyqf1U+hvHuqmKuPpePXdd/NpQIiZL6hGLLc1DQZJjRrKVvdl/mNcTandwWWp347Xb6h2E3V6ufTq847EuXrn6Pkm89ShN9GIUbWT7C9RuD9Tu6hxD7SYAtVtXltrdeO02NwyimLVgZKPi6Curzi2jbPJc7xERv39lMdGV2Og+ivzdrULtrs4x1O5ktL7Zk5AWJrtmDabechuya9Y0uiupyK1bjz2veRxdK3ob3RVCCCGkLmTXrMHWP3wEueXrG92VVHSv6KV2E0IIGVFk16zBzHufRG5tX6O7korMamo3Ic1Ay61E14WrSJMtPMoyHmUpVV2E4hCVPTyof/WglpZxST2zh6dBly2+lu0FrYiIi23bZWFcKl2BbttFFPJ5Z1upTqGEjJEI24YQNmwhfNZp76CitbrGtPiplt2sYbgW5qg6oqzreaXfAGK7rMVty0tUvK1U8bg07eos4d7yccMGqOfmywKf8HMkwahuicMxriq1uzZQu1tHu716pq6EUr/z2RirmrzHFYUoi4EedSy1m9qdtp+kBLU7GdTuIajdjddu4YRPMXQhWULimceNyR9VzvCubo8ZTobaTe1O209Sohra3XKT6DriirppmWWiohNNndtRmCtRErH292EomUk13ciSUou66y3iunPQfe5RD3NAemGP9UPbMH3l4oZxsYvFyMlz7+S4FzVW+lA9Q+V010W9x204kwKKOHgH8LjCq0tOootrFoYueUrBs1+dwIj7kFGJy65uoI7jiq/2z3WDS/kQEoZOxIM+X52QUMTjMZx/gEdB7a4O1O4h6qXdFkrf3f6YdXont+NOjkfh1Q2di7Luhxu1OxnUbqJC7aZ2Vwq1e4i6/e5OiDe8ihoDXTcBH9cgY1nldUYmKwW1OwnUbqJSDe1mOBdCCCGEEEIIIYQQQgghREPLrUSPsnzHsYyndaEKczPTWcJ1FlE1YYYs57VcprVOV+Jq16xEXQs1+3lYHUkt40C0y1mSRCfy2LTWctuOkfxTWVkuj5FuZepqd2+dUWFcJIZpwjDKr7fXSquziEZZlMuyiHv/l65moTWUJydx8brA1cBiG2XdVLNGFz2rE9U+RVno1XMMslZ7605DrROZkJEBtTsaand4Ha2u3e5KdM82VSdUPYisM6j+hHWU1UXtDtxP7SYjEWp3NNTu8DpaVbuDE4uGXxdLkxRUFxomKFmpijzWLgrYxXJNoHZTu6nd9achk+hS+OKGrQisI2IgrIeoWwCSnoHXfcy/vQbuR6aVWtDVa1JLV7Z6k+Zzj+tWpj6kBYm797PPjxmDt45cCGFZAByB1Xw/yie+i9pwLerkuXyfHxwM7b8ImEyXyH4ZzgOEZVnIjxmNR47bE7Ynw7lXlHRipMaikmgF2EOUMOkEDsp2bz3tsu6EbZW17XFVU0VddRtTRVx1EcsYRpnwq+53USLu7VfQ/0kIOq6WAhI3dtxwoFVcwand1O5G0mza/eI582FnzcRuoQUhtLqg6kHaCXEvQTrRv1EPHvkctdtbN7W7OlC7mw9qN7W7kTSbdr+0/86wLSt2OIahmOVmWfgWddLc1Eyil9UZMGEeJyZ6YWw3tVupm9pdHajdyWi5leiEDCtME0JatVtxBYNpopjjMEIIIWQEYZqwW1j7hGlQuwkhhIwsTBN2toW1j9pNSFPAbyEqczdSLaBRxLGEV+pOFratWhZyoLms5LXqS60zhWfWrsXYvz+JVbvsjHx3V6xj3FXmRTswIShQvgK9GDNES1Laenux4++fwYv7bYG+sZ0A/BZM1aIrCcs8HZeopBpRdXqt127ZAHd7LzpLviTMgq66kZX1J+QYb5+DysY9Z6C0kicMXbIZtS1CGg21Oxpqd23IrF2LTW98Cu9+ZBYGx8XTbi9RCa3ihmZJu0K9Y9UGzHrgZWq3A7WbkPpB7Y6G2l0bMmvXYupfX8C7201HcXRn6nosTegVSdSKdO9KdBmiRV2dLrd7Q7hYazZgx9+/Qu12oHaTRtHQSfRquJdVkyhRl6jZwwH9oJ/UfSyJKEW51oUdU8+4bWnd9xpNJUIu3cmi3MyMQgHt7yyDUSiXD3UiXOLGAcznY/TDP3muY2gyfqic/NzC7jOzUMT411ciMzjUxzTuQHHcyRCzTBJhk0RNRKhuP+r7SrKGqyS5BlFlgz45XV+jxLvMPS+k3ZHkEjYSoXbr+xAHandtqZd2d7/2PpYP6ttKMsEdFC89Tvk8pNsyoP6mz0I//mYGqd0AtVuF2j28oXbr+xAHandtqZt2r1iL94VIHBooiLRhXAxPOUs5Rtgi8Bi7KGDwdzcAarcKtbv+NI9JkxBCCCGEEEIIIYQQQghpMoZtOJdaWny9Vu0g63jUMV5qbQFvJtRzDbOQN4vLmmrBjnIjVMvHyRoeF3cFiT2UEV3e57rVJa5rpPNeuMf6y1eyWsFUEpnEsYJGuWepVMPCGpQ5OyjZSdB7ieoSJs/bez6qtTzKbV/XP++xqntZ0uuRjfH5VNONjJbwcnSfJfFD7W4+qN3p0OkLEO1mnKQuifT4jvAyd6F2U7urccxwh9odD2p380Htrg62szLctm1PyKHg1eLuMcVg/VDLecPCFJXwLW67nvfCFtRuULurccxwp9baPWwn0euFTqSlyFci4o0U73q4gpmWOeyykUuxTyPi8oFQurOZpvrw499umhZMU7l+Th2F/KB/uzJp7sYblHV6Pu+hiXl/+4bTrrynTcvylYkzgGvFSf6jiFQSd/i4GbPDjlVFMqp99Xz6I8LmhLUdNMhHPeyo/auG8DabEA83FzX+IG8OqN2VtUHtTo4utiYwNB5klLAuhYQ/OAPbC3hN+gOc2q1vm9odDLWb1AJqd2VtULsBIQRsWwzFIne2SzOQGz7FLo9VLpTJ8SHTkbNITQnvIvGGZAHKQ7iEYdvBekbtpnbXAmp3PDiJTkgDKXZ2YtUuO6PYmT65SSPJt7fjzX23xEBPrtFdIYQQQupCsbMTyz6yNfI97Y3uSioGenJ4idpNCCFkBFHs7MS7201Hob2t0V1JRbGT2k1IM9DQSfRaJDapZ+KOMKISmYTRTO5jtbZQVzMjOlD9zz9pFvjEdXd1om+brWFgKBmFawnXHOeuSLcs9wusnncG/ocDuV9anosJvntyFbt3BTpQWhEvOjrw7k6bOG3C14YX1bVKfR/k8pWUqGOqaVFVrbTyfIL6kLTdICt43DqirMfe7bW0hA83K3YtKArRsivaqN3BULtjHDeMtHvdLpvBQMCDtNQB3TgshLtPXX0lx3/pnixXpvc7x6pjq+rGHNieuloMgOhso3aD2p20H4TaXVYntbuqULtrr92rZ0wKLeeuUFdWj9umgFxxPlS49CKKpX4bzjGW7axIN6Xumr73bvLQkPhrakgYuyhgt2Xx7vbUbmp3sn6Q6mt3XSfR65ENvBFZsJOgE696C7jaXrNeL0nc62OaVupzGYpzFv94NQRLEopFG+bAALreXYa+KVMgcn6rshvvT+mPGeNBUfc9ME2/u6Owy8PB2PZQuBh/f/3x1wHAHBzE5BfexaoZ49Hf7h9OCsDQZIJCzgkNowqX+iAQNkAljSWmCkzGMPRuWZpJkKJGpLz1yHPIKPvUY9SBvJqZxsOIEvFqikI13N6GM63iHk7tpnanZThr96hn38H6mRNgd2QBBIxvSmgWSbtpumNjQaM1sq4+R6Plenc1vEsWpffZkCEkSIMyfXlMXLqS2k3tDoTaHQ61ewhqd8x+ULvLaJh2v7UC6zcaA7utNGrqwrqoFPI2TGVyfAjb9yIn1TNtTpgeN666EppNnZQPwBsT3RzIY/IL71O7qd2BULvDqaZ2t3YgLEJaHKu3F+MeW4xMb2+ju5KKbF8ftv7jC8it7Y8uTAghhAwDrN5eTL7rWWTX9DW6K6loX9tP7SaEEDKisHp7MfkfLyPbN9DorqTC6h2gdhPSBAzbmOj1sIxXw+WqWVzImnUlQZrrUy1rf1Dm9yjLt5rYRJYPyyJv2wLFou0eqyZJCVt57iYMNaWlu3TMUBJS/7EyzIssJweAoieJqWkHtyfK6g6//4MGl7iuTJmI/XHqkCRNWuKrU7GMtzvnrHOhLwgROajqLOFh56HWmdayHOZWVg90156WciKhdieD2l1O3bRbiDIXaaB8PIt1FppV6+pKqXb53tEi32qsEF3yvkaNt9TuYKjd5VC7iYTanQxqdzn10m4dcVeke5Er0stCwDi/mYtFuXLdGxLG06ZnZbq6ul3WJVe/A0BYPlJqdzDU7nKo3ZUzbCfRJWlEShXpWmbKbjairleQuMqy1XRVq+ZDjja0iSYTujeunupiFiXKuizhQQ8BurrUY2U59dHRNE1XpHWorpxDn5UZuL/UXryHVNu2IWwbNvyx1uIIsToxEFeskgz6qnDEEfWoh4uCUi4ojm3QpAqQ3I2skoeh0Hpr6EaWlpEey61VXMPrCbU7GdTuxmu3tm73wBjfb81kukolY2XB+aN2U7srhdpN7VahdieD2t182h02me7uU8K6mEpsc/W9e7wdPaGstlW2HdRuandlULsr126GcyGEEEIIIYQQQgghhBBCNDSDMaQuVOI2pbOcVqM/zUqS/unKprGQ1/K6JLWMA+mSnngJS4Ai7CJs08DghPEQVvlXUbWu61akx0G7wiFBNnu1rGmaQDaL9ZNHwcharjUzzKqnulDprOehLlYVWk2TuJWplm/1fVlCloCkKXnFaq5ry30foz9RdRAyXKB2J4PaXaIe2t03ZTTsrP68dYlGSw1EjNkxV6SHtqds92qLkbWo3cpx1G5Cqge1OxnU7hJ10e6x3RAJQgMJZ0V4kvAuKnFWoOvKeFekiwy1Wz2O2k0awYiZRK8G1YjFNpJplgcY3UNGLd0JvS5k3nhtxTFj8e7+H/WVtZR+6FzVvOjik8twL/KcTdP/Xp67HTOEC+CfTC+MHo0XDtql1E9nm1e0oty0yranEPW48b7CRLwa7loAShMfGpezKNKIuK58q9Gq/SatAbW7MqjdJVTtfmvBBxLV5XU/jhvipUwXHK3OVDjJnh/fhWePo3a7ULtT0ar9Jq0BtbsyqN0lVO1+Y8/tAo+Rk9i6OOdxcOtwjkkyAR810V4sCuQ726ndXqjdqWjVfjcTdZ1Ed5MgprQuVqUPptV0STxIYwmzlKcVdHUiXFIs2q6w68okQfedkttNdXuaFSG6FQ9O/8virHnFPGAbkD4WV1D5aiY4SWtxDovzFvthQ91fQf+a47FZD8W7taB2k2aklbVbRTu2K0m24qBblRYZD5TaHas9ajdpFajdpBlpNu1WJ6/VyXSJKAoYzuR4MWLCeyiBqLN4zfEjD/omFDWxz73teuHvbmp3Uqjd1YcmXkIaSHblSkz99W+QXbmy0V1JRW71anzoir+g6911je4KIYQQUheyK1di1jf/jNyytY3uSiq63l1H7SaEEDKiyK5ciVl3/hW5Nb2N7koqsivXU7sJaQJGXDiXRlvDq5r9uglWGIwULCUeeNLs4ZKMaQXGaUtCkIuaLrZ5eRzzJDHQg21s3jpMsxSTLWcYKIZZxmNaQGuRLVoXX62a+OpULeA1tP5GnYsaC68adceJbUeiqSQj+EiE2k3S0AzarWpQNoZelOlVjBXpSeJ/5gyD2q3WSe0mMaB2J4PaTdJQS+0WorTaPE6ccsAf1kVdFa67E8rrtn0voqhfxypDsartA4BZKKKN2k3tJomptnaPmEn04SjiuvcU93QkSYKjirskTiIUr/AbhgUYRuk1ss3y2OhqAhX1AU99n8lmS+8VgQ67P8OSjxqmBROlgSksvllS169K0PY2Rd1qXeqnG+Q6pktYokuKohVHw0g8QMcZZaKuddRn4N1PYU8Of4Ang9pNomiUdpsojYdFzQ/GOOOjN066F+04rEymh8Yv1W03DFiGQe0GtZvEh9qdDGo3iaJhv7tT4pvMVuKkF5VY6HZEiJah3+mGZ1vEMc7EP7Wb2k3iUyvtZjgXQgghhBBCCCGEEEIIIURDQ1ai61a2DidLbi0yYoetCNaVG07X1EvcawFUdg1kSJO4iU50lnJJmMVc55oWx4VcXZGuUrZC3YxvPwsra5omTMdy256gTp2rVxKLeNJvmCwfdIXi1uWWS+CqprMaq9vlSsRsDItzsyQISZKJfaQzHFaxUbtT1kntdhmO2i2p5bhcVneKRF0q1G5qt4TarYfa3RpQu2vLcNJuYQuIovCFaUmLDO9ixFyBrhIUUkatw7u63S4Kajeo3RJqt55aa3dThXOpZayxJC5DaeptVobLg1MS8a64rYB7Jamo61DF3h4/DssPOxR2V1dF9UZNtOtcD+NMpofFWxcTxuOVj+4B9OQSCUwlbmRqb3Tu8Gp8WLm/GneSKuqSjGFEC5rijh8YJzcCtQ15HeVdEHaOSa95ks8qaWb0kcBw+AEeBbW7+lC7U7RVZ+1+87S9YI/KRY57laCrOzSMSxzX4IndeOVUareE2k3tVqF2V1g3tdsHtTukrTpr98v77IBCe9vQtpiT6UH7jYhjdHHXw8K4qMd43w90d1C7PVC7qd0q9dLupppEJ2TEYVkojhpV+r/FHrAAQFgWBsd2NrobhBBCSP2wLBTGta72iQy1mxBCyAjDspDvam90L1IjLJPaTUgT0JST6EHWz2pZcE3Tqtgq3uxW8Dg0MsN4Pa3b3vaSnmvQvVIty7jEWrcOPU//H9btuAOKnbUXxahkOEGfjXYlumkis349NrvzWby310zkx3SUldFd8WpaRqMs4VHbg+qIS9CVsRTrtKTMii1fpRVZKZ9B8utUy29WLbOshzESreitCrW79lC7YxxXJ+3e6I5/YuVeM1HQ/KDVuU/HWbFejXEvLOlYdnUfJj/6CrXbu43aXVWo3a0Dtbv2ULtjHFcn7Z7y9CtYvtUmyHdWbzLdMv3jXdTq9qCwL2Ur0JUyRVsg29vP393qNmp3VaF2x6MpJ9GDqGYm7OEgxtUiSlirKfb1FnFJNe6Vih8ArfKwKXbRhjk4iM5XX0PvNrMBzSR6VLzzsDJquBbb9j+ERGWcD6vLtCxYxSJGPb8M7++6GWCU+p9WGL3osnOH3UFJ3LIqJcyVTXf+kS72zms1RFP2L00dzRJfjSI+PKB21wZqdzj10O7u597B6t2mVVR/XHRxMNOEdbEAtPXnMYba7dtO7a4O1O7hAbW7NlC7w6mHdo9+ewVWzpiEfNl+/8S3nNQ2zfJ45+rkeFETtiUNQZPnbrv5IrVb2U7trg7U7mTEz0hACCGEEEIIIYQQQgghhIwwYq9Er1WCkLQ00i1qJFGN69yKlnAV9f4PsnCX2ozvbmZaJgxPPWryk6CM4qVypqeMvz25Ij3KOiZXlcuV6ZUkGLVQssDW8ptYyR2UJoN3FGFWf9cyryQyUftTieU5Kqu62r9qWpfTWOwrsczTMl4Z1O6RCbW7RD20WxK1WjxoTCtLcKXRBXlFosb+IIKSgmUNg9qtQO0uh9rdOKjdIxNqd4laarcQ+oSfpTqjE426ZZTtaqLRNCvU1WOEZ2W6KApqtwK1uxxqd+1JHM5FdclqFnEfzjRKDCul0f1uxAOfKvJxxN0wjcAf5HJSXU6mB4VskRPqusn0svAuCcK3lPezvGzQNq/Llc49TEdUBvAwty2VJCKgcxOLQnV3ywtR5t6myyjeKMoeIjT9ihKHghCpQgyMNIoyO32Drwm1u/40WgPT0uh+t4p2myiNk4Oa/XF+uKllVP2QmpTmE0nqak3tpnZTu4egdo9cGq2BaWl0v1tFu92yymS1aQZ/173l1DLqhLsIiHUO6DVUVz6oDbUvEmo3tZvaPUS9tLtlYqITMhyxOzqwbscdYHeUJwdpBez2dizfc3MUunKN7gohhBBSF+yODqzacyaKXW2N7koqCl05ajchhJARhd3RgZVbboJCLtvorqSimMtSuwlpAiqeRG82d7NWpdHW4yiSWJeb/VwqIemKEJ37mXt8ZyfW77hDqazcpljR1RXpSQhaKZ4UK+wcerqx+oNbAChP0BFkvZa9SXomXitz1Oq6NNbYuJbwSka5sk+iBhZSXSKYoJWQulWSatbyMJGoRzKU4WJlL6rfhSZZ3Ubtroxm1ztqd4laaPfquTMBeLQvde/0JFlNnmiMHNWO1XtRu+NA7U4Htbs2ULurQ7PrHbW7RC20e/mWmwTvUxKJJkFNBjrUn+AV6rryYf0CgMG2LFbvNQMAtTsKanc6qN3xqNpKdIp6OlpN+Fqtv7Wm0vveGBxE2/LlGJw4EaIt3oq2sLAuEjW8i25/6AS5pl3ftsE8Rr32Pvo3GYPBttJ+r0ikddeKM3FQ6eCeRIiSfLpeF7hQlPbVmLdJ0H0r1b4kCSuQRNTrwXAR9WaD2p2OVtPCVutvramGdne8sgL9U8dA5EqjY9Rkelh4l2qOa3HqMgcKaH97NbXbA7W7NlC7awO1Ox2tpoWt1t9aUw3t7ly2GhvGdgO58NExKDZ60on2qMnysNjsQZj5In93K1C7awO1O5z4M2iEkKpjrVuHcX9+ANa6dY3uSioy69ZhkxufQnbVhkZ3hRBCCKkL1rp1mHRT62pfdtUGajchhJARhbVuHaY9+SLaevsb3ZVUZDf0U7sJaQJiGzlk4kMRkbDBNK26WsWZLby+2MVimVVcXvtms5YPl3tCF8bFMK2yxKGSqBXm6v5qhH2pJmFW5SiLqG5/kPuUmsxNtdynuYPiut+XlQuxVuvcxeIStiJBZwlXiWMZr4e1erhYxotC1MUtnNpNAGp3PVBXoOtWIAWNXXHLNvv4R+32Q+329KPJ7924ULup3fWE2l0/koRx0SUlTbqyHIgf2sW7Ir7aULv9ULs9/aB2B5LYUyCuqNcbinr90F3jZhP1etwT1XxwVWOhx4mBrk5+q5Pq9Zgct+EXyqDJgLTuZbFdtBBf5OsRTyxOPyTqA4UXef7N8I1qtFuZynAQ9XrGWqV2E2r3ENXU7riT52HE/VGVZHtUP6jdyaB2VwdqdzKo3YTaPUQ1tNswDN+EtDp5nmayWp08TxLzPHFboHYngdpdHajdfhjOhRBCCCGEEEIIIYQQQgjRENvIYTkrWouOBdBQwkF4LeSNTHaic3kiI5dmXS1hF23YwkC+uxu2GLKEqSvQ5eryqFXn1cAKWM0QtiJeWCYGx3ZCJEhQCpRnC5fvVauwakEPsn4mtYh6LeNRVvJKLNFxrfm1zGpeCUkszo20Sg8Hy7hEtZBXA2o3aVWaXbsHxnai4Pk+JVltpRu3kq5AD0O3+gqgdodB7a4P1O5wqN2kVWl27S525SBMoyor0OuNMAxqtwZqd32gdpdI7CmginoYzZA5nOJOJM0i6t6wLYWxY/DOwR8vvXH6pZscr9ekeVQZ76R6YcwYvHvanqU3IYOqLgaaTsQlHWb5Q0K1Bm1vbDZJlPtb3LhrScvqCJsESUs1+iVpJgEdTqJeC6jdpFVpVu1+w9E+OfakGZ+DYobWg8GJ3XiT2l1xWR3U7vhQu8OhdpNWpVm1+/X5OwJIPmnunXRPE/u8GgyO6sSbx1C7Ky2rg9odn5Gu3QznQgghhBBCCCGEEEIIIYRoiL0SXbUm6tzMghKfmJrkhs3gdualnpbSZkkCkhTTshpuUW5lvNbwYrGIzKpVmPzn+7H8Ix9GfvSoRHUVPXVZGrcuXSiYapFZtQqbf+8hvHXMzsBGPb59Xuut6r6ls8rGdW1PWkbXr6T907VZDTf+JES538U5Lm22ddJaULvr149mhtpdGUHavdlVD+KtY3ZGYWJ31drR6UM1dMOrc23vrcPU3z5J7aZ2kyaF2l2/fjQz1O7KCNLuWQ88hTc+OBsDozoBlCcWjYM8RrciXa5yj5NgVFuH0i+7KJBbu4G/uzV1RrVP7SbVJHE4l2q65jSD25mXergdtaqIh9Fq51RP9zI74OHWGw7FsAXM/n6gWAgoV35sWqo5me4N75IxDGT78sgIYFAp5xWDpAImy9VbUKJitcWJVxs1CVKL/sV1Fav29WxGV65m7FMzQO2uThvDiVY7p2bTbmPDYCydDvsBGXVMrSbTMwLUblC7m0knm7FPzQC1uzptDCda7ZyaTbutwQIgRKrJ86QkmUxXUSfXTdOAIQS1G9TuZtLJZuxTPYi/Et2JkWTbpcFJHcC9lvEgq3grESVOaUSg1QQvjOF0LtUmSLy9eIVc2EUIUX4vJZk8161Aj4vsT5zY6FGEiVfaJCS1IE6CE3V7XEu9t86wdislrP045aL2DQfCPouRBLV7CGr38DmXalMN7ZZUY4yvhKgHe9VkT+2mdjcT1O4S1O4hqN3D51yqTRrtNox4q8/DJsCrFRM9ST22LXzlqd3U7mZipGk3Y6ITQgghhBBCCCGEEEIIIRoSx0SXSMu4xBurzdDFZ9ZYC9XYbc3iZqaDFmGi3qM6S3gxYPWEUI+1BYpFW7sCvZZxzYNWoJsJVrcblgkTpYFE9l6ucgvLFo6QMr79IfvKA+CkJ8oyXk2irO3VqDvt/jDczzVoX5XOpZJVFGH1jSTLuAq1ewhqN6mqdguBohCJVzklGY/ijmFJYjNmnD9qd2VtUbvL66lGXd76qN1DULvJSKaa2q1DrjyPVTZiJXvUCvNKVrJTuytri9pdXk816vLWNxK0O3FM9EqIK/LNFrONkKSoIu4VcO9kud3ZhWUf3hf5rs5IkQ+aTI8b+kWGfYmakE8ygQ4AxVGj8J9P7Yb8uM5Y5WPHZqtSGS9BLuz1EG8dadqK605Wy37UQ9SrXRepDGo3GSkk0e73jtsVAwHapyacUmNnhrm81jIWuq+P4zqp3Smhdke0Q+1uGqjdZKSQRLtX7r0dCqM6Ek2aJ0VNPJomNrpa1rQMDHS14z+HUbvTQO2OaIfanYi6TqITQvyIbBaDEyc0uhupEdksBqaOaXQ3CCGEkLohsln0tbD2ibYMtZsQQsiIQmSz6B/X0+hupEZkLGo3IU1A4pjopmWV/kyz9Oe8rwTDMgOt5aZpuX+ENAO2XfSt1LCLts+lrFgsBrqSDe23S4lNnD9z/TqMeeofsDZsqGm/a4XZ24tx978Ia22/u026ioeRUf6SHJuWWtbttmEY2r9GEtSHSvtVTbe+QoqQCM1UfytA7SYjmVpo9/gHXoRY04e8EL4/laDtckxS/3RUW0estf3Ubm8b1O50dVG7aw61m4xkaqHdGz3/OrKDgzAsA4ZnNbp8r243LUP7p++3PwmoXRSJVqEHtQsAmb5Bare3DWp3urqo3RVTtXs7SNCLGrcwbxw3L1LQg2K40dWMVIs0Wd7TEhaixS4WkendgFH/ehG906eh2BnPNSusTjVMi6U8JEfFVQ/Lch4U6sXs70fP31/H+u0moziqHYB/kI87wESKfzUzbDuvBU+9OqopALVoK+qYWj9E6FzMolzCkpxrNc+RrmrlULtJq9Bs2j3+xTeweptJKDjaF5egiXY15IukVmOVtWEQo5dQu2O1S+0u2x+rDWp3TaF2k1ah2bR77CvvYO2mE1HoyFXcnhqmpZJY53HIDOYxesnb1O447VK7y/bHaoPaHYvEK9EJIYQQQgghhBBCCCGEkJFC6pXouqzhpmW5FkcrpTsYLeNkuCGTmgRZyIUQgfd6NYlagR6HoFXqte63JMgqqku8FpcMol2jKrHEJ+1fnLZazTWqHv1Nk6RmOFvGo6B2ExKfMO2Oi26VeRBRY5J2lVHYMbFbrz7U7nh1NBvU7uaD2k1IfNJot4gRbsVQQrhEHZM0hEuzQO2OV0ezQe2uH7GfraV461xyTLN8UXuU+44q9jo3tOD2gkWdYk+iiLqXg0h6P0nRLgY8kMZtVzfxHeeBQA3jUgtMTUzFND/Yk7gf6cqmcdvS9TUoo7iujpFMPSZnwrKSB5ZPINTDUdRVqN1kuNBs2i0AeLckmSyPoppjUtwxlNo9cqB2Nz/UbjJcaDbtrgZxJtzrBbV75EDtbh4auUCFkBFPMdeGdVvMRDHX1uiupMLOtWPt3E1R7Mg2uiuEEEJIXSjm2vD+TlNR7GxN7S52ZKndhBBCRhTFXBtWz5iEYltrToEV2jJYO4vaTUijqXgECbMu6rKH6yyCauITwzIjw0UwgzhJSj0TnEh0q8eLXV1YucvOAIYSFESFXpH7vXVWI1xLFEGJRe3uLqzee3biutJaICuxXCZx8ypL2KHUUU3LeK2zY3vxXoNKrcG1ePwMc/NLYxkfDpbuWkHtJq1Gs2n3u3tvnarOoBXrtRyrgsbM4ugOvL8/tbsSqN1DULvrB7WbtBrNpt2rdphRKpNiNbl6TL0SikoKHTm8v//0xMdRu+P1o9p1U7uHL1X7PLyiLl3MZLy2sLJBeEU9LE4bIUlII+I6dzI1PngxZt1qH4xCAW29vSiMGoWkQ3rQxHk9wrj4KBTQtmwt8uO7ILL+/lRjIK3pxEJI3WUiKLdHHFvN7NeVoBPqSgQuiVhExbyrF3EfWIaTe1lSqN2k2WlW7W5fthYD47uQqWBFmzrmVHOsCuuVkS8i+34vtVtzXBjU7tpD7Y6G2k2anWbV7tzqXgx2t0MYzvemgtAsUZPnphNDXW3DNI1Yx6sYxSJ/d4ccFwa1u/aMJO2u84wbIcRLdu06TL73PmTWrm10V1KRWbMGm1z7V2Tf7210VwghhJC6kF27DjOvfwK5FtW+7Pu91G5CCCEjiuzadZj20DNoW9/f6K6kIre+n9pNSBOQePlMElexoKQngD+juO7YSlHdzZjwhNSDqKSfpmVp7/eokCxpVpnXI8yLpJWtiSpp3ceC3LbS4j0+7rVV26yGpbdZogY2Sz9aFWo3IXoq0W6VRq3kaqY6GwW1e4hm0cxm6UerQu0mRE81tTsJcVeQ61a5pwn/YheFrz5qN7W7ljRLP5qV2NdHdRUre68ReaA6Yl2pexmzh5NqkNadzBvH3PtdMUImxms5aW6FfF91yHNXY6ObKInFYOIamx9V1FX3siTHSnQiHyb+UaJcyYODru5mE8+yzO0N6UXrQe0mpL7arRIWFzTuD600P8ii4llSu+MdK6F2p4PanQ5qNyG10W5hh8dDr0Z882pOnqtQu+MdK6F2p4PaHQ7DuRBCCCGEEEIIIYQQQgghGqpuVAhzm0mb8MRLtSzj/n7RSj6caURW8NgYBuy2HAxr6KuorkCvRUgWaclPsyLdh2HAzmUgWtCjLGMYNU0yEtYuUJ0EJ2nqqGYyj6SJTGrZdpiYjaREJ2mhdpNmo9m1G7kMMqYJ2cu4SUKbYnwxQO1O0S5A7a5229TuyqB2k2aj2bXbzlolDVRWi6urxCtJOKojbUJRF/7uTtUuQO2udtsjXbtjT6KrQqwT5jCSZg+3PMJbVAQ3ypU2idjT5YxEkdadLIr82LF4+8iFgfvUyfOKJ7w9yP5XOpleGDcOb567X9X6VQvCBubUMdic10rcy3Tlak3sOG+yfMT+WHXVQSSr6XaW5H5oduGndpORTC21+6Vz5gOInjxv1BgRNgYObjyK2h2zfWo3tbsRULvJSKam2r3/LhFt136iN+1kev+oTrz5GWp3nPap3dTuWsJwLoQQQgghhBBCCCGEEEKIhppMopuW5UviUPbeNLUZxIPKAyXruJUgrIVhmYkSP5X6ZQW6nZHWwi4WfX+p6/GskDAtsyyhZjXIrFmDSXfdjcyaNVWvOwzLsnx/cQi6BpnVqzH5p48hu2J9LbpZERnDqLm1MoP4lle1P9Xon64Oub0W16AAvTW8IERkopZ6ufIF9bEW7dfznGoNtZs0klbT7s1/vhhtIdpXDw1KS3bFemp33LLUbmp3k0PtJo2k1bR7xoP/h7Z1GzztitgrwmXZuH/VJre+j9odtyy1m9pdQ2qaaFV1Eyt7rxF0b+bxSuNqBQl62rhuhCRFxjcvBtxzhmnBFEB2zVoYxWJZLPRmIfQhplhE24r1MArD+zsV5RoWGhesBv0paz9CsNWHiFh1Rmz3nldSMdO5mQXVE6dMULl608px3VSo3WSkE0e7cyvWwyzYkCXSfPfVcbZeepEtCmo3qN3Ubmo3QO0mw4c42t22vg9GwAR3VBiXakyKp6nD2y+jaFO7Qe2mdjdeu6ufWDTE0q0T9XoTlSSFsdpam2rdX6Zpae8BuXpbjdEm45gLzXFxkoSmiYWuTnSrseRI86A+GNRz8I/TVhJR0AlrXHGPUy5tXRnD0MZra7TwNiPUbtJoWk27TcOAFTDOxCHJj6W49UTF0ySVQe1OVo7aXR+o3aTRtJp2S5KsPk9LRcfWITb7SIDanawctTsezbn0lRBCCCGEEEIIIYQQQghpAqq2YCU01pqprpL1Wwt17ma2bYdmDk+Dmm2ckDDU1RFyxbdc6a2zjKuEuZeZpgHLMlHNteNqP5MeN9JImsk7URZp57XMOquxxlYS5yuJhVftV6usXkzzGUVd6+FkGU8KtZsMR+qh3TqqMZ5EhXkJGq9bZQyvJtTu1vncqd3VhdpNhiON1G6VWsQ0j9XuCFiBTu2mdrc6sT8/r8AGbXffx0xSGNhGg93NVOheRqqNGvdcjB6FFfvsjUJ3d1XCuOj26ybTK5k0N00LYtQYvHfETsiP6UhdT7WpZGCuh6irxBX5aqH2I6mIh4UZCHLtCq2rggeXOMeWfU7O+7IJqiqIerM+EFC7CamcIO1etnBHiLGdib/7aX44VfvHVn5MB7U7bt2a7dTudFC740HtJqRygrT7zZ23wkB7m7tNnbRu5snzwc4c3vs4tTtW3Zrt1O50ULuV9hvaOiEjHNHWhv6pUwEAzfkYH47ItaFvy40a3Q1CCCGkboi2NvS2sPaJ9iy1mxBCyIhCtLVh/aSxje5GauxshtpNSBOQehJdZwkPS+AQteZVTYAC+DOGxzk2CVGJTpoNM8Kljpb71sIwLZh9feh6+RX0bjETdkcyq3KSVeTVDNPivQ/NDX0Y9firWD9nE9jduaq1kYZGWCTTWMYbSSVW0zTJ85JkA9cdW0mbavuqZVxSjcQnreKiRu2uP9Tu4YXU7rGPv4a1c6ag6Ghf1He/XqtW4oxB5voBdD/zNrWb2h18DLW76aB21x9q9/DC1e4X38bqTSegmGuLPigCdfW4aYWPI5WGaskM5Pm7G9Ru7THU7rqR+LONciMTdlEr6HJ7kum8KFezKLGPI/LNKupR4p2m/EgS/Gq6KSaN0ab7DqjfD2tDH0Y9/TT6p0yB0d0dry81jluu3kfuOQeck9m3AaOf+Df6N5+AwZgTCV6aVQSTinTYOUe6qMlysVpKR71FvKyOBJ9zte6JghDajOxRwttoYa4F1O76Qe2ujFbR7nF/fRkDMydgsKc98Jh6TJqnHaMy6wcw9iFqN7U7og5qd8OhdtcPandltIp2b/Tim+idOLpsEj1JGBfdZHgt45nbtg2zrx9jH1pK7aZ2h9dB7a45IzOLICGEEEIIIYQQQgghhBASg4oXygRl/BaK1VVaANXt3mOiUMtEWchbjaTW70rboWW8NkRlDfdZwy0LlmXCNExYlgn1zq3FivM091kt781Wc8uqN2WJOBrSi/qi3hN5zb2RraK1utUypVcDand1oHbXjmbXbpVqjB+tsgqH2h0OtZvaXSuo3dWB2l07ml27hSitOm9UAtGkVPM7Ru0Oh9pN7Y5LTfvuxlrTbNe9l6gubEBAlnJloFazmav7LdNCMaaQ1VLwkoi3oWaWroL720gU9VqgTnTr3My8WMqDqWEaMCyzaSbN09KwmFQ1bLeSh41aPKBU0wVNV0dV24iZbX04MJzOhdqth9o9PKiGdpsojZetOYU0BLXbD7Wb2t2qULv1ULuHB9XQbpVahmBJQ9xJc2q3H2o3tbuu7Te0dUJGOHZbG/qmTYNoqzy5SSMQbW3o3Xpj2DkOJYQQQkYGdlsbeme1rvbZuQy1mxBCyIjCbmvD2kljUczWb5FZNSlmMuidQe0mpNGk/gaWWaYDrNdu2YDs35VSTVeheiY2qefK4DjQMh6PtNcnyvpd7OnB6vnzUtUdRr3us2JPD9bN26nieoaTe1m1ziFocI6yUmuzcnvqjKwjRt+8ban/e99HuYSluVbVdCdTUd3L4iQ6abQlPCnU7nRQu1uTWmr3qn13CC2TJPFVPVZ2+RjXhdWHU7u9ULup3c0MtTsd1O7WpJba/dbcLUtt1DgJaC3Id+aw+vDtKq6H2l0OtTsYancwsSfRowaDOINFGuGNOwjpxF19H9elDGi80KnuZOr2aj6ENPpcmx3dQ1gl18u0TKBYhNnbC7u9HajgYbdhD4nFIqy1/Sh2tQE1CEcTh2bK6Fyrh5EykamwnmoRJeLab4dynFegdQ8AJB3U7vpD7W4eaqndqvYl0aB66FVoG0UbVu8gtduB2k3tbjao3fWH2t081PZ39wCKbRkgxBCVllrnCDBsm7+7PVC7qd2NojHfPkIIACCzejU2vvkPyKxe3eiupCKzejWm/PBhZJevb3RXCCGEkLqQWb0am139CNpaVPuyy9dTuwkhhIwoMqtXY9ZDTyO3rq/RXUlFbn0ftZuQJiB9OJeA7OBAPAtcmAuar404dVXBvSzKwlxva7HOEh5VrpqJT8Kg1Tw5aZKGNpsLYj1Q3cuSuJs1k2U8DXHcyJK6fNXiWgR9FjpLeNx+0ApeP6jdtYPaPfyIo90NX1XeBFC7/VC7SbWhdtcOavfwI83v7kqo9Qr0WkHt9kPtJnGJH84lQizD9qsx2ZLEdYvbr6j+xXEnq4W7lqQWE6Kyn3HFv1rUwsWqHlQznp++DSdLfdH2vY917AicNK8m9RKuSsolajtJWU371RT1Ss5R7Uecb2Dab0Mt74NWfFCkdlcGtbvxNKt2myiNCYM161VrjjlJoXbrt1O7K4Pa7ZSldlcFancymlW7q0GrTppXE2q3fju1uzJaQbsZzoUQQgghhBBCCCGEEEII0RB7Jbouw3dYQpEoC6TqiiYt417rntZ9LaFV03KsuGGW8XpmC68G9baER9EqSVLqaRknyYhyJ2tmi2SlpEk+ksZi38hrGKe/uv7VMju4ippQppWhdjcf1O50ULubF2p3wmOo3TWB2k3triXU7nQMF+0ejqvPqd0Jj6F214RW1O6q9TVoYJTb1MFT+2CQItN42fsKBrhaupUlIa27WD367+1TVCw7Hc0u9nGo1jkUxo3DO5/6ZClDuFNns4d18X5/7YkT8eZ/fbjqGcJrlW27ViSJIadSSebuNO1GlU0j9uoxxYg24rhpyW+YvNvU+G21FPdWEvFKoXZXD2p381NN7X71S/tBWGZNxota/+jKb9xD7Qa1m9rdulC7qwe1u/mppna//JFdIMz441AzTZ7393Tizc9Qu6nd1O5G04p9JmT4YBiA5uG2JTAMINPC/SeEEEKSYhgQrax91G5CCCEjDcOAaDKPgkRQuwlpCuo6ipiW5V/FWiyGuvbU0u0nDaZpJV4lnOaYSjEss+yvWnUNB6LuuzhU63O11qzFuHv/BGvN2orrqjby+6r+ebHWrMHEXz+BzPu9ieouCBH4p5IxDN/fcKCg/FWDal6fuJ9NGJbzp/ZLV7e3jbjt5YUIzC6epr8kHGp3faB2h9Ns2j3pf5YgszKZ9kVRL63LvN9L7U4ItZva3WpQu+sDtTucZtPu6U/8C229/ZFlbdtuqlXoANDW20/tTgi1m9pdC+LHRA+ImxaXSHcyjftZGqL6aXkGYF2cNlW0VPepoBhkzZ4FPOqchotQtxpGIY/csndhFPLuNrtFwroAgJEvoP2NVTDyzfXgXS1UgYzjkjVShMR7nvJ/Ka5FZbvuVeJ1j1Nd5QpKmTI07mbeutR+qm1U8iDU7BnEqd3eNqjdpDoYhTw63lgFc7CIwYRjQDOMFUa+SO1WylO7qd3NBLXb2wa1m1QHo5BH18q1MAshxqQmmzj3Yhap3Wp5aje1uxFwBCeEEEIIIYQQQgghhBBCNKSOiV4Ll684dVazXSvCii0t5rrEIbVeKRxlpVb7H5YBPWndSepImlClUdnE6+GmGOfcosq0wgp0Ekwca7g66FbLtQyoLNFKGiq1hAf1M66FWbdf9qWeWcVbCWo3tZvaXU4a7TZRGodsjjUtD7Xbv53a3XxQu6nd1O5yqvG7m7Qu1G7/dmp3/Ug9iZ7GBSzKvUwtVw0qcVWTYhkl6kmohohWIuJJ647TRpTLmg7TtFpe0HQPdHEe9GR8N8MwGhLDr5FEiU6jXXTqiRyE6yXqlQq+V5iTirjuHL19UUUpygVMzSZOwqF2p4PaPQS1e2TptRdq9xDUbmp3PaF2p4PaPQS1O1y7mzmMS6VQu4egdlO7q0HiSXRd7LMkoqkTdfXYsMFM9iNuP6MeIML6kUTUmz3GWdQqgKjjqvngANA6XOzqwpo9dkexq6vRXSkjzsN3sbsLKw/cFsVR7anaGEmiXQm1sKLrRF23XX2fFyJSxHX9DX2QkPeEpl+NJCjeW6tA7aZ2VxNqdxfeP3BbFFJqX6MpjmqndtcBandz3CvUbmp3o6B2NxfFri68s800DOQyLTlpnm9vw8oPU7trDbW7Oe6VZtbu1CvRCSGVI9rb0bfVVo3uRmpEezt6d5ja6G4QQgghdUO0t2P9jq2rfXZnG3pbuP+EEEJIUkR7O1ZNndjobqSm2JaldhPSBDR0Ej2um1ngsdLSHWEZT4POuq+zjFeDIEu1zvpcbas0EL06QWKZVm3ar6FlvB4x2dJi9Pej/Y030L/ZZhDtzbWiLc730ujvR9fTb6Fvq41gd7bVoVcjh7DBOa4rWpxs2EktvEnc0eLEYgOG4qn50FjGVZrVQj2coXaX1+2F2l0dml27u59+CxsSaF8zjVXmhkF0vPQetbsGULup3c0Ktbu8bi/U7urQ7No99q3lWLvRGBTbso3uTmKswTx/d9cIaje1OwmxJ9F1AldJ7DNJLQfbWoh9EnTCF8e9K02ctKR16Qj7XGvlYgaUxzQb7u5mVm8vRj/2OPLjx6PQZJPocR62rfW9GPPAc1g2aRTFvEJqadGMI3j1SoriJVDEI9D20znHRsVoi5uYpd5Qu9NB7U7GSNTuMQ8+h8FJozAYoX3NNiYAgLW2H+P+SO2uBtTueFC7k0HtTge1OxkjUbs3eW4p+kZt05KT6Nn+QWp3laB2x4PaHUxzBxIjhBBCCCGEEEIIIYQQQhpIbCOM4VgqdbPu3lWraS3cqhXWa82uNPlDGtc1bV2aJC+A3hWtVQlzN9NZxuO628XJJj7SE6A0A5W4fw434mQ3r4VluZoZxMvqjuhvNc5H5waXda5nHMt4lMW5We7ORlvGVajdnrqo3QCo3fWiWcYAQu1OC7W7cVC7PXVRuwFQu8nIg9qdDmp37Yk9iW45A7QcUuVwrU6w2cViYlczVTC874OEPW1dKlEucokyiyvZyKPOPU65arjsVfthIuizUduI624WJvpxhD4u1biOhMQlSvCTEFfEdZm+45RNul+SNYyhuGkRwpVE4GQZVZzc7ZpXX7+agGb5QU7tDofaPQS1uzIa/V0nlUHt1tdJ7a4/1O5wqN1DULvJSIbara+T2l07GM6FkAYiMhkMTpoEkWlojt/UiGwG/ZuNhcg2iz2SEEIIqS0ik0H/tHGwW1T7RNaidhNCCBlRiEwGvWN7YLeod7VtUbsJaQYSz9zpLONeklqUdeW81nW1bt2x8n2YBV21XsftT73QnZNqga5GgpEoa3FZX8KuaxX7J63l0jJeDfeyZrSMF0ePxsqPHdDobsQi6HtdHD0ay4/brVFdIhqSWGPTWu7TJCdRBUda/L39TGsJrxdJLd3NvqrNC7W7MqjdI0u7359f0r5WNIEXxndRu5sQanftoHYPQe0Ob5/aPby1+7Xdtm50N1Iz2NWO5cdt3+huEAVqd+1oVu1O/ewfR9STEiQUUeKtbpd1hAlyNQf1ILc6bz/Kyivx3Cp5cNC5c4W5kkVdP62oB5yPGqNO99Ch9k/X/6AytaCpRF0IoGgDlhnpmlMPEt+PQgCFYtP0f7ihi2lWVq6KrmwqYXXG/Qbp+qeen1eQ4oq27o4NOq6agprElc9bvhV+kCeF2p0Manc6mk67W1n7muzZY7hB7aZ2Vxtqt79/1G5qdyqEgFG0IUyjNbWv1Z89mhxqN7U7LgznQkgDyaxciUk33IDMypWN7koqMu+vxKaX34/su+sa3RVCCCGkLmRWtrb2Zd9d19L9J4QQQpKSWbkS297/JNrXbWh0V1LRvm4DtZuQJiDxSnSZLVyomaE9WcSjLI6uBVWxsgYdF9s1TWMJN8Ksw7FqjofOMl7WZkiG8UoJsjAntbhHlQ9NGBOR5KXVM6YPZ9KszLCLRWZvryLVyAReDWtrlFU9yJ2sUkt8Gku4RN6BaUYXXRtBFvpq0ahVbdTuYKjd1G5CKoHa7fxP7a4J1O5gqN3UbkIqgdrt/E/tTkXivqoiruIVT0sj/CqhWcI1oqd7EJDtW0oG6qCHkLTTf0HnkzbDeBJR14lkEsFWr1tU+0EPSanPtRncuDw0lXtZlUj6WVTi1mhalhszj6SnGiJeDXSCrIvBVgw5RkeU+1vGMLQip9vOOzAe1G5qN7WbkOpB7fbvp3bXBmo3tZvaTUj1oHb791O708FwLoQQQgghhBBCCCGEEEKIhtgr0YtOtmbV0pyGqMzfvrKaRCE6C/jQ+2A7ibelTMQqWtXyLa+B14VOEpV5vBIrdiVJUcKyesfaLxPHBLSps4yrSV7Crk2UNVrNFl4LhoNlvNrugyScWiYUqRdRCTqqcW5R1u2wNpJaxrNVdNNK406WNOFJvaB2U7t1+6jdZKRB7Y4HtbvxULup3bp91G4y0qB2x4PaXT8S97kYY1CNciOrJqqIZ7PZwHJSUCzLQjHloO1mRg+4BlEx16IEuBZCEiXUocdqRDyszjQudc0kpIkesKrU38KYMXjvyCNgt7enOr7Rk+GFsWPwn8/NQ7GrraH9aEbiimPcTODesipxXdO8fYj7QCJFMsi9THeOlQiZemyz/KBtdajd1G4d1O7kFMa0tvblJ3a3dP9rCbU7HdTu2kDtpnbroHYnpzBmDP49bwcU22oRfbn2DHR34D/HUruDoHang9qdjtYcQQgZLlgW7K6uRvciPZaF4qh0BgBCCCGkJWl17bPM1u4/IYQQkhTLQqG9dSeghUntJqQZiD2JXksrdxKrqOpOFuVG5h7nsVhrXc4SWjkN03Jdy1RXqiii3NC8dVYDXWZ2FV1/vMfp3Mdi9yXBedXSnawSqmXJt9atQ8+SJ7Fu151R7OlJ1YdGYq1dh/G3PI3V87dCcWxno7tTd5K4l0VZyCuxaEZZ1ZO4b6lWbfm+zG3LU6f8FsS1XodZzpNawN1voNLPghDajOPNYGX39q+WULvLoXZTu6uh3a2sfdaqDRjz4Est2/9KoXaXoHYnh9pN7U7UHrW7KlRTuzd9+mUs22pT5Dtz1ehaXcluGGjpZ49KoXaXoHYnp9raXfFK9GoKTlDMtrgThWo26zT9UkVerUE+0Hjj08kyqpxFxWIL62eUe1aSzOJuewlF3HUr02Rj9/6vE3X1gaUZXMeqTdxYfDqMwUG0v74U63fYvprdqhvG4CA6//Uu1u6+edn3ZSSRJlZbLeK7JXFRq5SsYbiuZlGjtHpv6M49jrjpHgR0ol5vmuFBIQ7Ubqd9pSy1m9odh1bXPnOg0NL9rxbU7nCo3c0HtdtpXylL7aZ2x8EYHMTod1dh+eaTkUfrTaJbBWo3QO2mdg/RKO2uPFsJIYQQQgghhBBCCCGEEDJMqWtM9LJs1yldnIDobOU6K6xpmpEWZde6XmbhLdXpdT+TPVQtPpYmA7na76Islx/U9ifoHEr9iecqFkYaS3jsujVZwr0W9DTW/VaglqsAmiGMCwkmYxiJLdytnHFctToHJUEByi3mOgt5HNTr1WjXrFZZvVYJ1G5qN0DtJsMXaje1ezhC7aZ2A9RuMnyhdlO7G0VNJ9GjBmpVjOJkmTYUkYyMyRbkqqbJ6K26pmnr9B7vxoqLN2iX91eKeCnJhTcGXpRbVhoR18ZeixBx9bp7+xp0jeP0YSQJXdB1T3r+nDwf3tRS1NPEkNPFaPOiE++k/arGsfLbIR8wMoahrb8WDwLDCWp3OdRuardkJJ0/iYbaXdmx1O7qQe0uh9pN7ZaMpPMn0VC7Kzt2pGt3TSbRG2HldMUoQihCY4slfDDw1aURwagBO5OVGaJLol5EdIydWsTDi/sgY1kmilVMOqI+yA03wq6naVlAdzfW77oL0N3dkg85dmcHVs/bEsXu1osrVyuawcKti9EWJl66OGm68wgTcnkHR1nCk5AmjlstaXT7tYDaTe2O3T61u6W1r9ida+n+1wJqN7W7VaF2U7tjt0/txrtbTkUh16Yt18wUcm1Y/SFqtxdqN7W7EdQ1nAshxI/d2YkNO+zQ6G6kxu7sxPodNm90NwghhJC60eraZ3fnsG6P1u0/IYQQkhS78/+3925BsiTnYd5fVd1zPWfOmXP2ziWxuC1IcAECvAZAAiR1McgwRepCmhdbYZuGoAgxZIdf5AjpyY+OsB8cfrCCVjhkSwzJBm1RDlIhG4QtEVSQIEXwziUIgqQWALm7Z89tZs7cuqvKD9PZU52dWZlZl66q7u+LmD073VVZ2dU99VXnn3/+e3LvHc933Y3KTLfHuBugB3gPoutR47IIpi1tK/RYxePpmNKcyvrljEx6UJbGlVnSsjLPyPj89aTZ/P9VS03MMLBF80fjsbkfGq618Ew418BLkqXPVZqVn6d41r/Msd1QiC4uZOuNN+Ty2Wcl3x5eVDm6uJCdP3hTLr7mUPKdsXuHDaJqZDwkat0kVfpri3z3nT5Es1fVB9yNu0PB3W6G7r7ofCLbrz0cbP/bBHf3F9xtBncvP467zX3C3Rdy881H8uTwhmTj4c0ljSfTQd97tAnu7i/r6O7gq4e6+JZdqPUCIb5rtOn7F1GSsUlFl8KSvAptRhZ55y7hlhZc0daMixfltFwsJVvod3H/6WRi7buJ0hsr7Xzo6WO+8lbbFdeO0/fN10SwqyQ5PpHbn/q03P+B75fpAAfRk+MTuf3zvy6v//iHZPIcMm8b11pjS9ur5xvsg0onK/6161L3lbup3/prrJNOpq/BZtvXJsI2pN/VjQTuNoO7cXcVhu6+0aMzefqnh9v/oYG7cXdVcLcZ3I27q5Acn8jbfv0L8ocfeq+cD3AQfevsAnevENyNu21ULy8NAAAAAAAAAAAAALDmhM9ED0hx0qO+OrZ0rmL18Hmke/acHnlRvy8VGNF+V9HsxBIBFhGJtRSrzKOQh54GpdqPtYiyfgbUs6OtrdmxrrdQkeVU29aX4uwAV1qebYaBLVIexcn1uZ+dH1sk3DUjIktT52ckmvUvT/VZE+uVXlYktPAO9BtXhHcVx6gSGXf1M5Hl61oTaWarLAyzykh41+DuRXA37gYoA3eHgbvbAXcvgrtxN0AZuDsM3F0N/zXRtQtymaD1C3SjVa1n/+ofmiwzp5PpEtclK3ItBn3fLF7stxJHmeR1uavjqX/n+zrSzkREotnxp7Mss1Cpx0lirV6uJG6TtkJPl0s7quZtk/o6w+D5erKKKuK2Y4zELnS1rb5PlX52sWbb/MpU6G+olH2211PV+g7uxt0iuBugLri7HXC3GdyNu0VwN0BdcHc7bLq7Wc4FoEPyJJHp7duSD3TAPE8SmTy1L3mFAjgAAABDZOjuy5N40P0HAAAIJU8Sudjfldyx7n1fyWPcDdAHKhcW1X83VQ9f2raQLmZ6XlFsa/7/emrSfN+rtCwVPZ5HgGcpYnokPI7tqVbWNDePKLAqjqIi8PNId+G4i22aI7txkkh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+      "text/plain": [
+       "<Figure size 1500x450 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Create tiles with different offsets to make overlap differences visible\n",
+    "left_offset = left_tile.copy()\n",
+    "left_offset.values = left_tile.values + 50   # shift up by 50m\n",
+    "left_offset.attrs['crs'] = 'EPSG:4326'\n",
+    "\n",
+    "strategies = ['first', 'last', 'mean']\n",
+    "fig, axes = plt.subplots(1, 3, figsize=(15, 4.5))\n",
+    "\n",
+    "for ax, strat in zip(axes, strategies):\n",
+    "    result = merge([left_offset, right_tile], strategy=strat, resolution=0.08)\n",
+    "    result.plot.imshow(ax=ax, cmap='terrain', add_colorbar=False)\n",
+    "    ax.set_title(f'strategy=\"{strat}\"')\n",
+    "    ax.axvline(-1, color='red', linewidth=1, linestyle='--', alpha=0.5)\n",
+    "    ax.axvline(1, color='red', linewidth=1, linestyle='--', alpha=0.5)\n",
+    "    ax.set_axis_off()\n",
+    "\n",
+    "plt.suptitle('Left tile has +50m offset; overlap zone is between the dashed lines',\n",
+    "             fontsize=11, y=1.0)\n",
+    "plt.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "g3aqegqlk8q",
+   "metadata": {},
+   "source": [
+    "Between the dashed lines you can see the difference: `first` shows the brighter (offset) left tile, `last` shows the darker right tile, and `mean` splits the difference. Outside the overlap zone, all three are identical."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "jftso1a3epq",
+   "metadata": {},
+   "source": [
+    "## Cross-CRS merge\n",
+    "\n",
+    "`merge` gets more useful when the input rasters are in different projections. Each one gets reprojected to the target CRS before combination, so you can feed in tiles from different sources directly.\n",
+    "\n",
+    "Here we take the left tile in EPSG:4326 and reproject the right tile to EPSG:3857 first, then merge both into a single EPSG:4326 output."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "ses9n3r9dml",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-03-19T20:56:26.185244Z",
+     "iopub.status.busy": "2026-03-19T20:56:26.185133Z",
+     "iopub.status.idle": "2026-03-19T20:56:26.305322Z",
+     "shell.execute_reply": "2026-03-19T20:56:26.304782Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Left tile CRS:  EPSG:4326 (degrees)\n",
+      "Right tile CRS: EPSG:3857 (meters)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x400 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Right tile reprojected to Web Mercator\n",
+    "right_tile_3857 = reproject(right_tile, 'EPSG:3857')\n",
+    "\n",
+    "print(f\"Left tile CRS:  EPSG:4326 (degrees)\")\n",
+    "print(f\"Right tile CRS: EPSG:3857 (meters)\")\n",
+    "\n",
+    "# Merge into EPSG:4326\n",
+    "cross_merged = merge([left_tile, right_tile_3857],\n",
+    "                     target_crs='EPSG:4326', resolution=0.08)\n",
+    "\n",
+    "fig, axes = plt.subplots(1, 3, figsize=(15, 4))\n",
+    "\n",
+    "left_tile.plot.imshow(ax=axes[0], cmap='terrain', add_colorbar=False)\n",
+    "axes[0].set_title('Left (4326)')\n",
+    "\n",
+    "right_tile_3857.plot.imshow(ax=axes[1], cmap='terrain', add_colorbar=False)\n",
+    "axes[1].set_title('Right (3857)')\n",
+    "\n",
+    "cross_merged.plot.imshow(ax=axes[2], cmap='terrain', add_colorbar=False)\n",
+    "axes[2].set_title('Merged (4326)')\n",
+    "\n",
+    "for ax in axes:\n",
+    "    ax.set_axis_off()\n",
+    "    ax.set_aspect('auto')\n",
+    "\n",
+    "plt.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "tb4guf56jtk",
+   "metadata": {},
+   "source": [
+    "The middle panel has axes in meters (EPSG:3857) while the others are in degrees, but `merge` handled the reprojection internally. The output is a clean mosaic in the target CRS."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7qemwcre8ke",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-block alert-info\">\n",
+    "<b>Dask support.</b> Both <code>reproject</code> and <code>merge</code> work with dask-backed DataArrays. Each output chunk is computed lazily and independently, so you can reproject datasets that don't fit in memory. Pass <code>chunk_size=512</code> to control the output chunking.\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "aqedo3vv20j",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-03-19T20:56:26.306637Z",
+     "iopub.status.busy": "2026-03-19T20:56:26.306535Z",
+     "iopub.status.idle": "2026-03-19T20:56:26.465296Z",
+     "shell.execute_reply": "2026-03-19T20:56:26.464784Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Preview saved.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Generate preview image for the notebook index\n",
+    "import os\n",
+    "import matplotlib\n",
+    "matplotlib.use('Agg')\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "_nb_dir = os.path.dirname(os.path.abspath('__file__'))\n",
+    "# Handle both notebook and nbconvert execution\n",
+    "_img_dir = os.path.join(_nb_dir, 'examples', 'user_guide', 'images')\n",
+    "if not os.path.isdir(_img_dir):\n",
+    "    _img_dir = os.path.join(_nb_dir, 'images')\n",
+    "os.makedirs(_img_dir, exist_ok=True)\n",
+    "\n",
+    "fig, axes = plt.subplots(1, 3, figsize=(15, 4.5))\n",
+    "\n",
+    "terrain.plot.imshow(ax=axes[0], cmap='terrain', add_colorbar=False)\n",
+    "axes[0].set_title('Source (EPSG:4326)', fontsize=13)\n",
+    "\n",
+    "terrain_3857.plot.imshow(ax=axes[1], cmap='terrain', add_colorbar=False)\n",
+    "axes[1].set_title('Reprojected (EPSG:3857)', fontsize=13)\n",
+    "\n",
+    "cross_merged.plot.imshow(ax=axes[2], cmap='terrain', add_colorbar=False)\n",
+    "axes[2].set_title('Cross-CRS Merge', fontsize=13)\n",
+    "\n",
+    "for ax in axes:\n",
+    "    ax.set_axis_off()\n",
+    "    ax.set_aspect('auto')\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "fig.savefig(os.path.join(_img_dir, 'reproject_preview.png'),\n",
+    "            bbox_inches='tight', dpi=120)\n",
+    "plt.close(fig)\n",
+    "print(\"Preview saved.\")\n",
+    "\n",
+    "matplotlib.use('module://matplotlib_inline.backend_inline')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9zl6nz3lzna",
+   "metadata": {},
+   "source": [
+    "### References\n",
+    "\n",
+    "- [Map projection](https://en.wikipedia.org/wiki/Map_projection), Wikipedia\n",
+    "- [Web Mercator projection](https://en.wikipedia.org/wiki/Web_Mercator_projection), Wikipedia\n",
+    "- [EPSG:4326](https://epsg.io/4326) (WGS 84 geographic), epsg.io\n",
+    "- [EPSG:3857](https://epsg.io/3857) (Web Mercator), epsg.io\n",
+    "- [pyproj documentation](https://pyproj4.github.io/pyproj/stable/)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.14.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/examples/user_guide/images/reproject_preview.png b/examples/user_guide/images/reproject_preview.png
new file mode 100644
index 00000000..a6da14ed
Binary files /dev/null and b/examples/user_guide/images/reproject_preview.png differ
diff --git a/setup.cfg b/setup.cfg
index 2e47c5e9..85c1a741 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -56,6 +56,8 @@ optional =
     spatialpandas
     # Optional for gpu_rtx functions. Also requires cupy.
     rtxpy
+reproject =
+    pyproj
 dask =
     dask[array]
     dask-geopandas
@@ -69,6 +71,7 @@ tests =
     noise >= 1.2.2
     dask
     pyarrow
+    pyproj
     pytest
     pytest-cov
     scipy
diff --git a/xrspatial/__init__.py b/xrspatial/__init__.py
index 97a9ce62..1ebe0736 100644
--- a/xrspatial/__init__.py
+++ b/xrspatial/__init__.py
@@ -120,6 +120,8 @@
 from xrspatial.zonal import regions as regions  # noqa
 from xrspatial.zonal import stats as zonal_stats  # noqa
 from xrspatial.zonal import suggest_zonal_canvas as suggest_zonal_canvas  # noqa
+from xrspatial.reproject import merge  # noqa
+from xrspatial.reproject import reproject  # noqa
 
 import xrspatial.accessor  # noqa: F401  — registers .xrs accessors
 
diff --git a/xrspatial/accessor.py b/xrspatial/accessor.py
index 89d521f2..51eb1007 100644
--- a/xrspatial/accessor.py
+++ b/xrspatial/accessor.py
@@ -345,6 +345,12 @@ def standardize(self, **kwargs):
         from .normalize import standardize
         return standardize(self._obj, **kwargs)
 
+    # ---- Reproject ----
+
+    def reproject(self, target_crs, **kwargs):
+        from .reproject import reproject
+        return reproject(self._obj, target_crs, **kwargs)
+
     # ---- Raster to vector ----
 
     def polygonize(self, **kwargs):
diff --git a/xrspatial/reproject/__init__.py b/xrspatial/reproject/__init__.py
new file mode 100644
index 00000000..c1bc327f
--- /dev/null
+++ b/xrspatial/reproject/__init__.py
@@ -0,0 +1,703 @@
+"""Out-of-core CRS reprojection and multi-raster merge.
+
+Public API
+----------
+reproject(raster, target_crs, ...)
+    Reproject a DataArray to a new coordinate reference system.
+merge(rasters, ...)
+    Merge multiple DataArrays into a single mosaic.
+"""
+from __future__ import annotations
+
+import numpy as np
+import xarray as xr
+
+from ._crs_utils import _detect_nodata, _detect_source_crs, _resolve_crs
+from ._grid import (
+    _chunk_bounds,
+    _compute_chunk_layout,
+    _compute_output_grid,
+    _make_output_coords,
+)
+from ._interpolate import _resample_cupy, _resample_numpy, _validate_resampling
+from ._merge import _merge_arrays_cupy, _merge_arrays_numpy, _validate_strategy
+from ._transform import ApproximateTransform
+
+__all__ = ['reproject', 'merge']
+
+
+# ---------------------------------------------------------------------------
+# Source geometry helpers
+# ---------------------------------------------------------------------------
+
+def _source_bounds(raster):
+    """Extract (left, bottom, right, top) from a DataArray's coordinates."""
+    ydim = raster.dims[-2]
+    xdim = raster.dims[-1]
+    y = raster.coords[ydim].values
+    x = raster.coords[xdim].values
+    # Compute pixel-edge bounds from pixel-center coords
+    if len(y) > 1:
+        res_y = abs(float(y[1] - y[0]))
+    else:
+        res_y = 1.0
+    if len(x) > 1:
+        res_x = abs(float(x[1] - x[0]))
+    else:
+        res_x = 1.0
+    x_min, x_max = float(np.min(x)), float(np.max(x))
+    y_min, y_max = float(np.min(y)), float(np.max(y))
+    left = x_min - res_x / 2
+    right = x_max + res_x / 2
+    bottom = y_min - res_y / 2
+    top = y_max + res_y / 2
+    return (left, bottom, right, top)
+
+
+def _is_y_descending(raster):
+    """Check if Y axis goes from top (large) to bottom (small)."""
+    ydim = raster.dims[-2]
+    y = raster.coords[ydim].values
+    if len(y) < 2:
+        return True
+    return float(y[0]) > float(y[-1])
+
+
+# ---------------------------------------------------------------------------
+# Per-chunk worker functions
+# ---------------------------------------------------------------------------
+
+def _reproject_chunk_numpy(
+    source_data, source_bounds_tuple, source_shape, source_y_desc,
+    src_wkt, tgt_wkt,
+    chunk_bounds_tuple, chunk_shape,
+    resampling, nodata, transform_precision,
+):
+    """Reproject a single output chunk (numpy backend).
+
+    Called inside ``dask.delayed`` for the dask path, or directly for numpy.
+    CRS objects are passed as WKT strings for pickle safety.
+    """
+    from ._crs_utils import _require_pyproj
+
+    pyproj = _require_pyproj()
+    src_crs = pyproj.CRS.from_wkt(src_wkt)
+    tgt_crs = pyproj.CRS.from_wkt(tgt_wkt)
+
+    # Build inverse transformer: target -> source
+    transformer = pyproj.Transformer.from_crs(
+        tgt_crs, src_crs, always_xy=True
+    )
+
+    height, width = chunk_shape
+    approx = ApproximateTransform(
+        transformer, chunk_bounds_tuple, chunk_shape,
+        precision=transform_precision,
+    )
+
+    # All output pixel positions (broadcast 1-D arrays to avoid HxW meshgrid)
+    row_grid = np.arange(height, dtype=np.float64)[:, np.newaxis]
+    col_grid = np.arange(width, dtype=np.float64)[np.newaxis, :]
+    row_grid = np.broadcast_to(row_grid, (height, width))
+    col_grid = np.broadcast_to(col_grid, (height, width))
+
+    # Source CRS coordinates for each output pixel
+    src_y, src_x = approx(row_grid, col_grid)
+
+    # Convert source CRS coordinates to source pixel coordinates
+    src_left, src_bottom, src_right, src_top = source_bounds_tuple
+    src_h, src_w = source_shape
+    src_res_x = (src_right - src_left) / src_w
+    src_res_y = (src_top - src_bottom) / src_h
+
+    src_col_px = (src_x - src_left) / src_res_x - 0.5
+    if source_y_desc:
+        src_row_px = (src_top - src_y) / src_res_y - 0.5
+    else:
+        src_row_px = (src_y - src_bottom) / src_res_y - 0.5
+
+    # Determine source window needed
+    r_min = int(np.floor(np.nanmin(src_row_px))) - 2
+    r_max = int(np.ceil(np.nanmax(src_row_px))) + 3
+    c_min = int(np.floor(np.nanmin(src_col_px))) - 2
+    c_max = int(np.ceil(np.nanmax(src_col_px))) + 3
+
+    # Check overlap
+    if r_min >= src_h or r_max <= 0 or c_min >= src_w or c_max <= 0:
+        return np.full(chunk_shape, nodata, dtype=np.float64)
+
+    # Clip to source bounds
+    r_min_clip = max(0, r_min)
+    r_max_clip = min(src_h, r_max)
+    c_min_clip = max(0, c_min)
+    c_max_clip = min(src_w, c_max)
+
+    # Extract source window
+    window = source_data[r_min_clip:r_max_clip, c_min_clip:c_max_clip]
+    if hasattr(window, 'compute'):
+        window = window.compute()
+    window = np.asarray(window, dtype=np.float64)
+
+    # Convert sentinel nodata to NaN so numba kernels can detect it
+    if not np.isnan(nodata):
+        window = window.copy()
+        window[window == nodata] = np.nan
+
+    # Adjust coordinates relative to window
+    local_row = src_row_px - r_min_clip
+    local_col = src_col_px - c_min_clip
+
+    return _resample_numpy(window, local_row, local_col,
+                           resampling=resampling, nodata=nodata)
+
+
+def _reproject_chunk_cupy(
+    source_data, source_bounds_tuple, source_shape, source_y_desc,
+    src_wkt, tgt_wkt,
+    chunk_bounds_tuple, chunk_shape,
+    resampling, nodata, transform_precision,
+):
+    """CuPy variant of ``_reproject_chunk_numpy``."""
+    import cupy as cp
+
+    from ._crs_utils import _require_pyproj
+
+    pyproj = _require_pyproj()
+    src_crs = pyproj.CRS.from_wkt(src_wkt)
+    tgt_crs = pyproj.CRS.from_wkt(tgt_wkt)
+
+    transformer = pyproj.Transformer.from_crs(
+        tgt_crs, src_crs, always_xy=True
+    )
+
+    height, width = chunk_shape
+    approx = ApproximateTransform(
+        transformer, chunk_bounds_tuple, chunk_shape,
+        precision=transform_precision,
+    )
+
+    row_grid = np.arange(height, dtype=np.float64)[:, np.newaxis]
+    col_grid = np.arange(width, dtype=np.float64)[np.newaxis, :]
+    row_grid = np.broadcast_to(row_grid, (height, width))
+    col_grid = np.broadcast_to(col_grid, (height, width))
+
+    # Control grid is on CPU
+    src_y, src_x = approx(row_grid, col_grid)
+
+    src_left, src_bottom, src_right, src_top = source_bounds_tuple
+    src_h, src_w = source_shape
+    src_res_x = (src_right - src_left) / src_w
+    src_res_y = (src_top - src_bottom) / src_h
+
+    src_col_px = (src_x - src_left) / src_res_x - 0.5
+    if source_y_desc:
+        src_row_px = (src_top - src_y) / src_res_y - 0.5
+    else:
+        src_row_px = (src_y - src_bottom) / src_res_y - 0.5
+
+    r_min = int(np.floor(np.nanmin(src_row_px))) - 2
+    r_max = int(np.ceil(np.nanmax(src_row_px))) + 3
+    c_min = int(np.floor(np.nanmin(src_col_px))) - 2
+    c_max = int(np.ceil(np.nanmax(src_col_px))) + 3
+
+    if r_min >= src_h or r_max <= 0 or c_min >= src_w or c_max <= 0:
+        return cp.full(chunk_shape, nodata, dtype=cp.float64)
+
+    r_min_clip = max(0, r_min)
+    r_max_clip = min(src_h, r_max)
+    c_min_clip = max(0, c_min)
+    c_max_clip = min(src_w, c_max)
+
+    window = source_data[r_min_clip:r_max_clip, c_min_clip:c_max_clip]
+    if hasattr(window, 'compute'):
+        window = window.compute()
+    if not isinstance(window, cp.ndarray):
+        window = cp.asarray(window)
+    window = window.astype(cp.float64)
+
+    # Convert sentinel nodata to NaN
+    if not np.isnan(nodata):
+        window = window.copy()
+        window[window == nodata] = cp.nan
+
+    local_row = src_row_px - r_min_clip
+    local_col = src_col_px - c_min_clip
+
+    return _resample_cupy(window, local_row, local_col,
+                          resampling=resampling, nodata=nodata)
+
+
+# ---------------------------------------------------------------------------
+# reproject()
+# ---------------------------------------------------------------------------
+
+def reproject(
+    raster,
+    target_crs,
+    *,
+    source_crs=None,
+    resolution=None,
+    bounds=None,
+    width=None,
+    height=None,
+    resampling='bilinear',
+    nodata=None,
+    transform_precision=16,
+    chunk_size=None,
+    name=None,
+):
+    """Reproject a raster DataArray to a new coordinate reference system.
+
+    Supports numpy, cupy, dask+numpy, and dask+cupy backends. For dask
+    inputs, the computation is fully lazy: each output chunk independently
+    reads only the source pixels it needs.
+
+    Parameters
+    ----------
+    raster : xr.DataArray
+        Input raster with y/x coordinates.
+    target_crs
+        Target CRS in any format accepted by ``pyproj.CRS()``.
+    source_crs : optional
+        Source CRS. Auto-detected from *raster* if None.
+    resolution : float or (float, float) or None
+        Output pixel size in target CRS units.
+    bounds : (left, bottom, right, top) or None
+        Explicit output extent in target CRS.
+    width, height : int or None
+        Explicit output grid dimensions.
+    resampling : str
+        One of 'nearest', 'bilinear', 'cubic'.
+    nodata : float or None
+        Nodata value. Auto-detected if None.
+    transform_precision : int
+        Coarse grid subdivisions for approximate transform (default 16).
+    chunk_size : int or (int, int) or None
+        Output chunk size for dask. Defaults to 512.
+    name : str or None
+        Name for the output DataArray.
+
+    Returns
+    -------
+    xr.DataArray
+    """
+    from ._crs_utils import _require_pyproj
+
+    if not isinstance(raster, xr.DataArray):
+        raise TypeError(
+            f"reproject(): raster must be an xr.DataArray, "
+            f"got {type(raster).__name__}"
+        )
+
+    _validate_resampling(resampling)
+    _require_pyproj()
+
+    # Resolve CRS
+    src_crs = _resolve_crs(source_crs)
+    if src_crs is None:
+        src_crs = _detect_source_crs(raster)
+    if src_crs is None:
+        raise ValueError(
+            "Could not detect source CRS. Pass source_crs explicitly."
+        )
+    tgt_crs = _resolve_crs(target_crs)
+
+    # Detect nodata
+    nd = _detect_nodata(raster, nodata)
+
+    # Source geometry
+    src_bounds = _source_bounds(raster)
+    src_shape = (raster.sizes[raster.dims[-2]], raster.sizes[raster.dims[-1]])
+    y_desc = _is_y_descending(raster)
+
+    # Compute output grid
+    grid = _compute_output_grid(
+        src_bounds, src_shape, src_crs, tgt_crs,
+        resolution=resolution, bounds=bounds,
+        width=width, height=height,
+    )
+    out_bounds = grid['bounds']
+    out_shape = grid['shape']
+
+    # Output coordinates
+    y_coords, x_coords = _make_output_coords(out_bounds, out_shape)
+
+    # Detect backend
+    from ..utils import has_dask_array, is_cupy_array
+
+    data = raster.data
+    is_dask = False
+    if has_dask_array():
+        import dask.array as _da
+        is_dask = isinstance(data, _da.Array)
+    is_cupy = False
+    if is_dask:
+        # Check underlying type
+        try:
+            from ..utils import is_cupy_backed
+            is_cupy = is_cupy_backed(raster)
+        except (ImportError, Exception):
+            pass
+    else:
+        is_cupy = is_cupy_array(data)
+
+    # Serialize CRS for pickle safety
+    src_wkt = src_crs.to_wkt()
+    tgt_wkt = tgt_crs.to_wkt()
+
+    if is_dask:
+        result_data = _reproject_dask(
+            raster, src_bounds, src_shape, y_desc,
+            src_wkt, tgt_wkt,
+            out_bounds, out_shape,
+            resampling, nd, transform_precision,
+            chunk_size, is_cupy,
+        )
+    elif is_cupy:
+        result_data = _reproject_inmemory_cupy(
+            raster, src_bounds, src_shape, y_desc,
+            src_wkt, tgt_wkt,
+            out_bounds, out_shape,
+            resampling, nd, transform_precision,
+        )
+    else:
+        result_data = _reproject_inmemory_numpy(
+            raster, src_bounds, src_shape, y_desc,
+            src_wkt, tgt_wkt,
+            out_bounds, out_shape,
+            resampling, nd, transform_precision,
+        )
+
+    ydim = raster.dims[-2]
+    xdim = raster.dims[-1]
+    result = xr.DataArray(
+        result_data,
+        dims=[ydim, xdim],
+        coords={ydim: y_coords, xdim: x_coords},
+        name=name or raster.name,
+        attrs={
+            'crs': tgt_wkt,
+            'nodata': nd,
+        },
+    )
+    return result
+
+
+def _reproject_inmemory_numpy(
+    raster, src_bounds, src_shape, y_desc,
+    src_wkt, tgt_wkt,
+    out_bounds, out_shape,
+    resampling, nodata, precision,
+):
+    """Single-chunk numpy reproject."""
+    return _reproject_chunk_numpy(
+        raster.values,
+        src_bounds, src_shape, y_desc,
+        src_wkt, tgt_wkt,
+        out_bounds, out_shape,
+        resampling, nodata, precision,
+    )
+
+
+def _reproject_inmemory_cupy(
+    raster, src_bounds, src_shape, y_desc,
+    src_wkt, tgt_wkt,
+    out_bounds, out_shape,
+    resampling, nodata, precision,
+):
+    """Single-chunk cupy reproject."""
+    return _reproject_chunk_cupy(
+        raster.data,
+        src_bounds, src_shape, y_desc,
+        src_wkt, tgt_wkt,
+        out_bounds, out_shape,
+        resampling, nodata, precision,
+    )
+
+
+def _reproject_dask(
+    raster, src_bounds, src_shape, y_desc,
+    src_wkt, tgt_wkt,
+    out_bounds, out_shape,
+    resampling, nodata, precision,
+    chunk_size, is_cupy,
+):
+    """Dask backend: build output as ``da.block`` of delayed chunks."""
+    import dask
+    import dask.array as da
+
+    row_chunks, col_chunks = _compute_chunk_layout(out_shape, chunk_size)
+    n_row = len(row_chunks)
+    n_col = len(col_chunks)
+
+    chunk_fn = _reproject_chunk_cupy if is_cupy else _reproject_chunk_numpy
+    dtype = np.float64
+
+    blocks = [[None] * n_col for _ in range(n_row)]
+
+    row_offset = 0
+    for i in range(n_row):
+        col_offset = 0
+        for j in range(n_col):
+            rchunk = row_chunks[i]
+            cchunk = col_chunks[j]
+            cb = _chunk_bounds(
+                out_bounds, out_shape,
+                row_offset, row_offset + rchunk,
+                col_offset, col_offset + cchunk,
+            )
+            delayed_chunk = dask.delayed(chunk_fn)(
+                raster.data,
+                src_bounds, src_shape, y_desc,
+                src_wkt, tgt_wkt,
+                cb, (rchunk, cchunk),
+                resampling, nodata, precision,
+            )
+            blocks[i][j] = da.from_delayed(
+                delayed_chunk, shape=(rchunk, cchunk), dtype=dtype
+            )
+            col_offset += cchunk
+        row_offset += rchunk
+
+    return da.block(blocks)
+
+
+# ---------------------------------------------------------------------------
+# merge()
+# ---------------------------------------------------------------------------
+
+def merge(
+    rasters,
+    *,
+    target_crs=None,
+    resolution=None,
+    bounds=None,
+    resampling='bilinear',
+    nodata=None,
+    strategy='first',
+    chunk_size=None,
+    name=None,
+):
+    """Merge multiple rasters into a single mosaic.
+
+    Each input is reprojected to the target CRS (if needed) and placed
+    into a unified output grid. Overlapping regions are resolved using
+    the selected *strategy*.
+
+    Parameters
+    ----------
+    rasters : list of xr.DataArray
+        Input rasters to merge.
+    target_crs : optional
+        Target CRS. Defaults to the CRS of the first raster.
+    resolution : float or (float, float) or None
+        Output resolution in target CRS units.
+    bounds : (left, bottom, right, top) or None
+        Explicit output extent.
+    resampling : str
+        Interpolation method: 'nearest', 'bilinear', 'cubic'.
+    nodata : float or None
+        Nodata value for the output.
+    strategy : str
+        Merge strategy: 'first', 'last', 'mean', 'max', 'min'.
+    chunk_size : int or (int, int) or None
+        Chunk size for dask output.
+    name : str or None
+        Name for the output DataArray.
+
+    Returns
+    -------
+    xr.DataArray
+    """
+    from ._crs_utils import _require_pyproj
+
+    if not rasters:
+        raise ValueError("merge(): rasters list must not be empty")
+
+    _validate_resampling(resampling)
+    _validate_strategy(strategy)
+    pyproj = _require_pyproj()
+
+    # Resolve target CRS
+    tgt_crs = _resolve_crs(target_crs)
+    if tgt_crs is None:
+        tgt_crs = _detect_source_crs(rasters[0])
+    if tgt_crs is None:
+        raise ValueError(
+            "Could not detect target CRS. Pass target_crs explicitly."
+        )
+
+    # Detect nodata
+    nd = nodata if nodata is not None else _detect_nodata(rasters[0], nodata)
+
+    # Gather source info for each raster
+    raster_infos = []
+    for r in rasters:
+        src_crs = _detect_source_crs(r)
+        if src_crs is None:
+            raise ValueError(
+                f"Could not detect CRS for raster '{r.name}'. "
+                "Ensure all rasters have CRS metadata."
+            )
+        sb = _source_bounds(r)
+        ss = (r.sizes[r.dims[-2]], r.sizes[r.dims[-1]])
+        yd = _is_y_descending(r)
+        raster_infos.append({
+            'raster': r,
+            'src_crs': src_crs,
+            'src_bounds': sb,
+            'src_shape': ss,
+            'y_desc': yd,
+            'src_wkt': src_crs.to_wkt(),
+        })
+
+    # Compute unified output grid
+    if bounds is None:
+        # Union of all raster bounds in target CRS
+        all_bounds = []
+        for info in raster_infos:
+            grid = _compute_output_grid(
+                info['src_bounds'], info['src_shape'],
+                info['src_crs'], tgt_crs,
+                resolution=resolution,
+            )
+            all_bounds.append(grid['bounds'])
+        left = min(b[0] for b in all_bounds)
+        bottom = min(b[1] for b in all_bounds)
+        right = max(b[2] for b in all_bounds)
+        top = max(b[3] for b in all_bounds)
+        merged_bounds = (left, bottom, right, top)
+    else:
+        merged_bounds = bounds
+
+    # Use first raster's info for resolution estimation if needed
+    info0 = raster_infos[0]
+    grid = _compute_output_grid(
+        info0['src_bounds'], info0['src_shape'],
+        info0['src_crs'], tgt_crs,
+        resolution=resolution, bounds=merged_bounds,
+    )
+    out_bounds = grid['bounds']
+    out_shape = grid['shape']
+    tgt_wkt = tgt_crs.to_wkt()
+
+    # Detect if any input is dask
+    from ..utils import has_dask_array
+
+    any_dask = False
+    if has_dask_array():
+        import dask.array as _da
+        any_dask = any(isinstance(r.data, _da.Array) for r in rasters)
+
+    if any_dask:
+        result_data = _merge_dask(
+            raster_infos, tgt_wkt, out_bounds, out_shape,
+            resampling, nd, strategy, chunk_size,
+        )
+    else:
+        result_data = _merge_inmemory(
+            raster_infos, tgt_wkt, out_bounds, out_shape,
+            resampling, nd, strategy,
+        )
+
+    y_coords, x_coords = _make_output_coords(out_bounds, out_shape)
+    ydim = rasters[0].dims[-2]
+    xdim = rasters[0].dims[-1]
+
+    result = xr.DataArray(
+        result_data,
+        dims=[ydim, xdim],
+        coords={ydim: y_coords, xdim: x_coords},
+        name=name or 'merged',
+        attrs={
+            'crs': tgt_wkt,
+            'nodata': nd,
+        },
+    )
+    return result
+
+
+def _merge_inmemory(
+    raster_infos, tgt_wkt, out_bounds, out_shape,
+    resampling, nodata, strategy,
+):
+    """In-memory merge using numpy."""
+    arrays = []
+    for info in raster_infos:
+        reprojected = _reproject_chunk_numpy(
+            info['raster'].values,
+            info['src_bounds'], info['src_shape'], info['y_desc'],
+            info['src_wkt'], tgt_wkt,
+            out_bounds, out_shape,
+            resampling, nodata, 16,
+        )
+        arrays.append(reprojected)
+    return _merge_arrays_numpy(arrays, nodata, strategy)
+
+
+def _merge_chunk_worker(
+    raster_data_list, src_bounds_list, src_shape_list, y_desc_list,
+    src_wkt_list, tgt_wkt,
+    chunk_bounds_tuple, chunk_shape,
+    resampling, nodata, strategy, precision,
+):
+    """Worker for a single merge chunk."""
+    arrays = []
+    for i in range(len(raster_data_list)):
+        reprojected = _reproject_chunk_numpy(
+            raster_data_list[i],
+            src_bounds_list[i], src_shape_list[i], y_desc_list[i],
+            src_wkt_list[i], tgt_wkt,
+            chunk_bounds_tuple, chunk_shape,
+            resampling, nodata, precision,
+        )
+        arrays.append(reprojected)
+    return _merge_arrays_numpy(arrays, nodata, strategy)
+
+
+def _merge_dask(
+    raster_infos, tgt_wkt, out_bounds, out_shape,
+    resampling, nodata, strategy, chunk_size,
+):
+    """Dask merge backend."""
+    import dask
+    import dask.array as da
+
+    row_chunks, col_chunks = _compute_chunk_layout(out_shape, chunk_size)
+    n_row = len(row_chunks)
+    n_col = len(col_chunks)
+
+    # Prepare lists for the worker
+    data_list = [info['raster'].data for info in raster_infos]
+    bounds_list = [info['src_bounds'] for info in raster_infos]
+    shape_list = [info['src_shape'] for info in raster_infos]
+    ydesc_list = [info['y_desc'] for info in raster_infos]
+    wkt_list = [info['src_wkt'] for info in raster_infos]
+
+    dtype = np.float64
+    blocks = [[None] * n_col for _ in range(n_row)]
+
+    row_offset = 0
+    for i in range(n_row):
+        col_offset = 0
+        for j in range(n_col):
+            rchunk = row_chunks[i]
+            cchunk = col_chunks[j]
+            cb = _chunk_bounds(
+                out_bounds, out_shape,
+                row_offset, row_offset + rchunk,
+                col_offset, col_offset + cchunk,
+            )
+            delayed_chunk = dask.delayed(_merge_chunk_worker)(
+                data_list, bounds_list, shape_list, ydesc_list,
+                wkt_list, tgt_wkt,
+                cb, (rchunk, cchunk),
+                resampling, nodata, strategy, 16,
+            )
+            blocks[i][j] = da.from_delayed(
+                delayed_chunk, shape=(rchunk, cchunk), dtype=dtype
+            )
+            col_offset += cchunk
+        row_offset += rchunk
+
+    return da.block(blocks)
diff --git a/xrspatial/reproject/_crs_utils.py b/xrspatial/reproject/_crs_utils.py
new file mode 100644
index 00000000..a4eb5be6
--- /dev/null
+++ b/xrspatial/reproject/_crs_utils.py
@@ -0,0 +1,77 @@
+"""CRS detection utilities and optional pyproj import guard."""
+from __future__ import annotations
+
+
+def _require_pyproj():
+    """Import and return the pyproj module, raising a clear error if missing."""
+    try:
+        import pyproj
+        return pyproj
+    except ImportError:
+        raise ImportError(
+            "pyproj is required for CRS reprojection. "
+            "Install it with:  pip install pyproj  "
+            "or:  pip install xarray-spatial[reproject]"
+        )
+
+
+def _resolve_crs(crs_input):
+    """Convert *crs_input* to a ``pyproj.CRS`` object.
+
+    Accepts anything ``pyproj.CRS()`` accepts: EPSG int, authority string,
+    WKT, proj4 dict, or an existing ``pyproj.CRS`` instance.
+
+    Returns None if *crs_input* is None.
+    """
+    if crs_input is None:
+        return None
+    pyproj = _require_pyproj()
+    if isinstance(crs_input, pyproj.CRS):
+        return crs_input
+    return pyproj.CRS(crs_input)
+
+
+def _detect_source_crs(raster):
+    """Auto-detect the CRS of a DataArray.
+
+    Fallback chain:
+    1. ``raster.rio.crs`` (rioxarray)
+    2. ``raster.attrs['crs']``
+    3. None
+    """
+    # rioxarray
+    try:
+        rio_crs = raster.rio.crs
+        if rio_crs is not None:
+            return _resolve_crs(rio_crs)
+    except Exception:
+        pass
+
+    # attrs
+    crs_attr = raster.attrs.get('crs')
+    if crs_attr is not None:
+        return _resolve_crs(crs_attr)
+
+    return None
+
+
+def _detect_nodata(raster, nodata=None):
+    """Determine nodata value from explicit arg, rioxarray, or attrs."""
+    if nodata is not None:
+        return float(nodata)
+
+    # rioxarray
+    try:
+        rio_nd = raster.rio.nodata
+        if rio_nd is not None:
+            return float(rio_nd)
+    except Exception:
+        pass
+
+    # attrs
+    for key in ('_FillValue', 'nodata', 'missing_value'):
+        val = raster.attrs.get(key)
+        if val is not None:
+            return float(val)
+
+    return float('nan')
diff --git a/xrspatial/reproject/_grid.py b/xrspatial/reproject/_grid.py
new file mode 100644
index 00000000..3a19aa99
--- /dev/null
+++ b/xrspatial/reproject/_grid.py
@@ -0,0 +1,258 @@
+"""Output grid computation and chunk layout for reprojection."""
+from __future__ import annotations
+
+import numpy as np
+
+
+def _compute_output_grid(source_bounds, source_shape, source_crs, target_crs,
+                         resolution=None, bounds=None, width=None, height=None):
+    """Compute the output raster grid parameters.
+
+    Parameters
+    ----------
+    source_bounds : tuple
+        (left, bottom, right, top) in source CRS.
+    source_shape : tuple
+        (height, width) of source raster.
+    source_crs, target_crs : pyproj.CRS
+        Source and target coordinate reference systems.
+    resolution : float or tuple or None
+        Target resolution. If tuple, (x_res, y_res).
+    bounds : tuple or None
+        Explicit (left, bottom, right, top) in target CRS.
+    width, height : int or None
+        Explicit output dimensions.
+
+    Returns
+    -------
+    dict with keys: bounds, shape, res_x, res_y
+    """
+    from ._crs_utils import _require_pyproj
+
+    pyproj = _require_pyproj()
+    transformer = pyproj.Transformer.from_crs(
+        source_crs, target_crs, always_xy=True
+    )
+
+    if bounds is None:
+        # Transform source corners and edges to target CRS
+        src_left, src_bottom, src_right, src_top = source_bounds
+
+        # Clamp geographic coordinates away from singularities.
+        # Exactly +/-180 longitude produces inf in many projections;
+        # latitudes beyond +/-90 are invalid.
+        if source_crs.is_geographic:
+            clamp = 0.01  # degrees
+            src_left = max(src_left, -180 + clamp)
+            src_right = min(src_right, 180 - clamp)
+            src_bottom = max(src_bottom, -90 + clamp)
+            src_top = min(src_top, 90 - clamp)
+            if src_left >= src_right:
+                src_left, src_right = source_bounds[0], source_bounds[2]
+            if src_bottom >= src_top:
+                src_bottom, src_top = source_bounds[1], source_bounds[3]
+
+        n_edge = 21  # sample points along each edge
+        xs = np.concatenate([
+            np.linspace(src_left, src_right, n_edge),   # top edge
+            np.linspace(src_left, src_right, n_edge),   # bottom edge
+            np.full(n_edge, src_left),                   # left edge
+            np.full(n_edge, src_right),                  # right edge
+        ])
+        ys = np.concatenate([
+            np.full(n_edge, src_top),
+            np.full(n_edge, src_bottom),
+            np.linspace(src_bottom, src_top, n_edge),
+            np.linspace(src_bottom, src_top, n_edge),
+        ])
+        tx, ty = transformer.transform(xs, ys)
+        tx = np.asarray(tx)
+        ty = np.asarray(ty)
+        # Filter out inf/nan from failed transforms
+        valid = np.isfinite(tx) & np.isfinite(ty)
+        if not valid.any():
+            raise ValueError(
+                "Could not transform any source boundary points "
+                "to target CRS."
+            )
+        tx = tx[valid]
+        ty = ty[valid]
+
+        # Detect antimeridian / polar blow-up: if the transformed extent
+        # is absurdly large relative to the source, use a densified
+        # interior grid instead of just boundary points.
+        raw_bounds = (float(tx.min()), float(ty.min()),
+                      float(tx.max()), float(ty.max()))
+        src_w_crs = src_right - src_left
+        src_h_crs = src_top - src_bottom
+        out_w_crs = raw_bounds[2] - raw_bounds[0]
+        out_h_crs = raw_bounds[3] - raw_bounds[1]
+
+        # Heuristic: if output span is > 50x source span in either axis,
+        # boundary points likely wrapped around a singularity. Fall back
+        # to a dense interior grid to get a tighter bounding box.
+        ratio_x = out_w_crs / (abs(src_w_crs) + 1e-30)
+        ratio_y = out_h_crs / (abs(src_h_crs) + 1e-30)
+        if ratio_x > 50 or ratio_y > 50:
+            # Sample a dense interior grid instead
+            n_dense = 51
+            ix = np.linspace(src_left, src_right, n_dense)
+            iy = np.linspace(src_bottom, src_top, n_dense)
+            ixx, iyy = np.meshgrid(ix, iy)
+            itx, ity = transformer.transform(ixx.ravel(), iyy.ravel())
+            itx = np.asarray(itx)
+            ity = np.asarray(ity)
+            ivalid = np.isfinite(itx) & np.isfinite(ity)
+            if ivalid.any():
+                itx = itx[ivalid]
+                ity = ity[ivalid]
+                # Use IQR-based bounds to reject outlier transforms
+                q_lo, q_hi = 2, 98
+                bounds = (
+                    float(np.percentile(itx, q_lo)),
+                    float(np.percentile(ity, q_lo)),
+                    float(np.percentile(itx, q_hi)),
+                    float(np.percentile(ity, q_hi)),
+                )
+            else:
+                bounds = raw_bounds
+        else:
+            bounds = raw_bounds
+
+    left, bottom, right, top = bounds
+
+    # Determine resolution
+    if resolution is not None:
+        if isinstance(resolution, (tuple, list)):
+            res_x, res_y = float(resolution[0]), float(resolution[1])
+        else:
+            res_x = res_y = float(resolution)
+    elif width is not None and height is not None:
+        res_x = (right - left) / width
+        res_y = (top - bottom) / height
+    else:
+        # Estimate from source resolution
+        src_h, src_w = source_shape
+        src_left, src_bottom, src_right, src_top = source_bounds
+        src_res_x = (src_right - src_left) / src_w
+        src_res_y = (src_top - src_bottom) / src_h
+        # Use the geometric mean of transformed pixel sizes
+        center_x = (src_left + src_right) / 2
+        center_y = (src_bottom + src_top) / 2
+        tx1, ty1 = transformer.transform(center_x, center_y)
+        tx2, ty2 = transformer.transform(
+            center_x + src_res_x, center_y + src_res_y
+        )
+        res_x = abs(float(tx2) - float(tx1))
+        res_y = abs(float(ty2) - float(ty1))
+        if res_x == 0 or res_y == 0:
+            res_x = (right - left) / src_w
+            res_y = (top - bottom) / src_h
+
+    # Compute dimensions
+    if width is None:
+        width = max(1, int(np.ceil((right - left) / res_x)))
+    if height is None:
+        height = max(1, int(np.ceil((top - bottom) / res_y)))
+
+    # Adjust bounds to be exact multiples of resolution
+    right = left + width * res_x
+    top = bottom + height * res_y
+
+    return {
+        'bounds': (left, bottom, right, top),
+        'shape': (height, width),
+        'res_x': res_x,
+        'res_y': res_y,
+    }
+
+
+def _make_output_coords(bounds, shape):
+    """Create y and x coordinate arrays for the output grid.
+
+    Coordinates are pixel-center aligned.
+
+    Parameters
+    ----------
+    bounds : tuple
+        (left, bottom, right, top) in target CRS.
+    shape : tuple
+        (height, width).
+
+    Returns
+    -------
+    y_coords, x_coords : ndarray
+    """
+    left, bottom, right, top = bounds
+    height, width = shape
+    res_x = (right - left) / width
+    res_y = (top - bottom) / height
+    x_coords = np.linspace(left + res_x / 2, right - res_x / 2, width)
+    y_coords = np.linspace(top - res_y / 2, bottom + res_y / 2, height)
+    return y_coords, x_coords
+
+
+def _compute_chunk_layout(shape, chunk_size):
+    """Compute chunk sizes along each axis.
+
+    Parameters
+    ----------
+    shape : tuple
+        (height, width).
+    chunk_size : int or tuple or None
+        Target chunk size. None defaults to 512.
+
+    Returns
+    -------
+    row_chunks, col_chunks : tuple of int
+    """
+    if chunk_size is None:
+        chunk_size = (512, 512)
+    elif isinstance(chunk_size, int):
+        chunk_size = (chunk_size, chunk_size)
+
+    height, width = shape
+    rcs, ccs = chunk_size
+
+    row_chunks = []
+    remaining = height
+    while remaining > 0:
+        c = min(rcs, remaining)
+        row_chunks.append(c)
+        remaining -= c
+
+    col_chunks = []
+    remaining = width
+    while remaining > 0:
+        c = min(ccs, remaining)
+        col_chunks.append(c)
+        remaining -= c
+
+    return tuple(row_chunks), tuple(col_chunks)
+
+
+def _chunk_bounds(grid_bounds, grid_shape, row_start, row_end, col_start, col_end):
+    """Compute geographic bounds for a specific chunk within the output grid.
+
+    Parameters
+    ----------
+    grid_bounds : tuple
+        (left, bottom, right, top) of the full output grid.
+    grid_shape : tuple
+        (height, width) of the full output grid.
+    row_start, row_end, col_start, col_end : int
+        Pixel indices of the chunk.
+
+    Returns
+    -------
+    tuple : (left, bottom, right, top) bounds of the chunk.
+    """
+    left, bottom, right, top = grid_bounds
+    height, width = grid_shape
+    res_x = (right - left) / width
+    res_y = (top - bottom) / height
+    c_left = left + col_start * res_x
+    c_right = left + col_end * res_x
+    c_top = top - row_start * res_y
+    c_bottom = top - row_end * res_y
+    return (c_left, c_bottom, c_right, c_top)
diff --git a/xrspatial/reproject/_interpolate.py b/xrspatial/reproject/_interpolate.py
new file mode 100644
index 00000000..1180a561
--- /dev/null
+++ b/xrspatial/reproject/_interpolate.py
@@ -0,0 +1,298 @@
+"""Per-backend resampling via numba JIT (nearest/bilinear) or map_coordinates (cubic)."""
+from __future__ import annotations
+
+import numpy as np
+from numba import njit
+
+
+_RESAMPLING_ORDERS = {
+    'nearest': 0,
+    'bilinear': 1,
+    'cubic': 3,
+}
+
+
+def _validate_resampling(resampling):
+    if resampling not in _RESAMPLING_ORDERS:
+        raise ValueError(
+            f"resampling must be one of {list(_RESAMPLING_ORDERS)}, "
+            f"got {resampling!r}"
+        )
+    return _RESAMPLING_ORDERS[resampling]
+
+
+# ---------------------------------------------------------------------------
+# Numba kernels for nearest and bilinear resampling
+# ---------------------------------------------------------------------------
+
+@njit(nogil=True, cache=True)
+def _resample_nearest_jit(src, row_coords, col_coords, nodata):
+    """Nearest-neighbor resampling with NaN propagation."""
+    h_out, w_out = row_coords.shape
+    sh, sw = src.shape
+    out = np.empty((h_out, w_out), dtype=np.float64)
+    for i in range(h_out):
+        for j in range(w_out):
+            r = row_coords[i, j]
+            c = col_coords[i, j]
+            if r < -0.5 or r > sh - 0.5 or c < -0.5 or c > sw - 0.5:
+                out[i, j] = nodata
+                continue
+            ri = int(r + 0.5)
+            ci = int(c + 0.5)
+            if ri < 0:
+                ri = 0
+            if ri >= sh:
+                ri = sh - 1
+            if ci < 0:
+                ci = 0
+            if ci >= sw:
+                ci = sw - 1
+            v = src[ri, ci]
+            # NaN check: works for float64
+            if v != v:
+                out[i, j] = nodata
+            else:
+                out[i, j] = v
+    return out
+
+
+@njit(nogil=True, cache=True)
+def _resample_cubic_jit(src, row_coords, col_coords, nodata):
+    """Catmull-Rom cubic resampling with NaN propagation.
+
+    Separable: interpolate 4 row-slices along columns, then combine
+    along rows.  Handles NaN inline (no second pass needed).
+    """
+    h_out, w_out = row_coords.shape
+    sh, sw = src.shape
+    out = np.empty((h_out, w_out), dtype=np.float64)
+    for i in range(h_out):
+        for j in range(w_out):
+            r = row_coords[i, j]
+            c = col_coords[i, j]
+            if r < -0.5 or r > sh - 0.5 or c < -0.5 or c > sw - 0.5:
+                out[i, j] = nodata
+                continue
+
+            r0 = int(np.floor(r))
+            c0 = int(np.floor(c))
+            fr = r - r0
+            fc = c - c0
+
+            # Catmull-Rom column weights (a = -0.5)
+            fc2 = fc * fc
+            fc3 = fc2 * fc
+            wc0 = -0.5 * fc3 + fc2 - 0.5 * fc
+            wc1 = 1.5 * fc3 - 2.5 * fc2 + 1.0
+            wc2 = -1.5 * fc3 + 2.0 * fc2 + 0.5 * fc
+            wc3 = 0.5 * fc3 - 0.5 * fc2
+
+            # Catmull-Rom row weights
+            fr2 = fr * fr
+            fr3 = fr2 * fr
+            wr0 = -0.5 * fr3 + fr2 - 0.5 * fr
+            wr1 = 1.5 * fr3 - 2.5 * fr2 + 1.0
+            wr2 = -1.5 * fr3 + 2.0 * fr2 + 0.5 * fr
+            wr3 = 0.5 * fr3 - 0.5 * fr2
+
+            val = 0.0
+            has_nan = False
+            for di in range(4):
+                ri = r0 - 1 + di
+                ric = ri
+                if ric < 0:
+                    ric = 0
+                elif ric >= sh:
+                    ric = sh - 1
+                # Interpolate along columns for this row
+                rv = 0.0
+                for dj in range(4):
+                    cj = c0 - 1 + dj
+                    cjc = cj
+                    if cjc < 0:
+                        cjc = 0
+                    elif cjc >= sw:
+                        cjc = sw - 1
+                    sv = src[ric, cjc]
+                    if sv != sv:  # NaN check
+                        has_nan = True
+                        break
+                    if dj == 0:
+                        rv = sv * wc0
+                    elif dj == 1:
+                        rv += sv * wc1
+                    elif dj == 2:
+                        rv += sv * wc2
+                    else:
+                        rv += sv * wc3
+                if has_nan:
+                    break
+                if di == 0:
+                    val = rv * wr0
+                elif di == 1:
+                    val += rv * wr1
+                elif di == 2:
+                    val += rv * wr2
+                else:
+                    val += rv * wr3
+
+            out[i, j] = nodata if has_nan else val
+    return out
+
+
+@njit(nogil=True, cache=True)
+def _resample_bilinear_jit(src, row_coords, col_coords, nodata):
+    """Bilinear resampling with NaN propagation."""
+    h_out, w_out = row_coords.shape
+    sh, sw = src.shape
+    out = np.empty((h_out, w_out), dtype=np.float64)
+    for i in range(h_out):
+        for j in range(w_out):
+            r = row_coords[i, j]
+            c = col_coords[i, j]
+            if r < -0.5 or r > sh - 0.5 or c < -0.5 or c > sw - 0.5:
+                out[i, j] = nodata
+                continue
+
+            r0 = int(np.floor(r))
+            c0 = int(np.floor(c))
+            r1 = r0 + 1
+            c1 = c0 + 1
+            dr = r - r0
+            dc = c - c0
+
+            # Clamp to source bounds
+            r0c = r0 if r0 >= 0 else 0
+            r1c = r1 if r1 < sh else sh - 1
+            c0c = c0 if c0 >= 0 else 0
+            c1c = c1 if c1 < sw else sw - 1
+
+            v00 = src[r0c, c0c]
+            v01 = src[r0c, c1c]
+            v10 = src[r1c, c0c]
+            v11 = src[r1c, c1c]
+
+            # If any neighbor is NaN, output nodata
+            if v00 != v00 or v01 != v01 or v10 != v10 or v11 != v11:
+                out[i, j] = nodata
+            else:
+                out[i, j] = (v00 * (1.0 - dr) * (1.0 - dc) +
+                             v01 * (1.0 - dr) * dc +
+                             v10 * dr * (1.0 - dc) +
+                             v11 * dr * dc)
+    return out
+
+
+# ---------------------------------------------------------------------------
+# Public numpy resampler
+# ---------------------------------------------------------------------------
+
+def _resample_numpy(source_window, src_row_coords, src_col_coords,
+                    resampling='bilinear', nodata=np.nan):
+    """Resample a numpy source window at fractional pixel coordinates.
+
+    Uses numba JIT for nearest and bilinear (fast path), falls back to
+    scipy.ndimage.map_coordinates for cubic.
+
+    Parameters
+    ----------
+    source_window : ndarray (H_src, W_src)
+    src_row_coords, src_col_coords : ndarray (H_out, W_out)
+    resampling : str
+    nodata : float
+
+    Returns
+    -------
+    ndarray (H_out, W_out)
+    """
+    order = _validate_resampling(resampling)
+
+    is_integer = np.issubdtype(source_window.dtype, np.integer)
+    work = source_window.astype(np.float64) if is_integer else source_window
+
+    # Ensure float64 and contiguous for numba
+    if work.dtype != np.float64:
+        work = work.astype(np.float64)
+    work = np.ascontiguousarray(work)
+    rc = np.ascontiguousarray(src_row_coords, dtype=np.float64)
+    cc = np.ascontiguousarray(src_col_coords, dtype=np.float64)
+
+    nd = float(nodata)
+
+    if order == 0:
+        result = _resample_nearest_jit(work, rc, cc, nd)
+        if is_integer:
+            result = np.round(result).astype(source_window.dtype)
+        return result
+
+    if order == 1:
+        return _resample_bilinear_jit(work, rc, cc, nd)
+
+    # Cubic: numba Catmull-Rom (handles NaN inline, no extra passes)
+    return _resample_cubic_jit(work, rc, cc, nd)
+
+
+# ---------------------------------------------------------------------------
+# CuPy resampler (unchanged -- GPU kernels are already fast)
+# ---------------------------------------------------------------------------
+
+def _resample_cupy(source_window, src_row_coords, src_col_coords,
+                   resampling='bilinear', nodata=np.nan):
+    """CuPy equivalent of ``_resample_numpy``.
+
+    Control grid is on CPU (pyproj is CPU-only); coordinates are
+    transferred to GPU for interpolation.
+    """
+    import cupy as cp
+    from cupyx.scipy.ndimage import map_coordinates
+
+    order = _validate_resampling(resampling)
+
+    is_integer = cp.issubdtype(source_window.dtype, cp.integer)
+    if is_integer:
+        work = source_window.astype(cp.float64)
+    else:
+        work = source_window
+
+    # Transfer coordinate arrays to GPU
+    if not isinstance(src_row_coords, cp.ndarray):
+        src_row_coords = cp.asarray(src_row_coords)
+    if not isinstance(src_col_coords, cp.ndarray):
+        src_col_coords = cp.asarray(src_col_coords)
+
+    if cp.issubdtype(work.dtype, cp.floating):
+        nan_mask = cp.isnan(work)
+        has_nan = bool(nan_mask.any())
+    else:
+        nan_mask = None
+        has_nan = False
+    if has_nan:
+        if not is_integer:
+            work = work.copy()
+        work[nan_mask] = 0.0
+
+    coords = cp.array([src_row_coords.ravel(), src_col_coords.ravel()])
+    result = map_coordinates(
+        work, coords, order=order, mode='constant', cval=0.0
+    ).reshape(src_row_coords.shape)
+
+    h, w = source_window.shape
+    oob = (
+        (src_row_coords < -0.5) | (src_row_coords > h - 0.5) |
+        (src_col_coords < -0.5) | (src_col_coords > w - 0.5)
+    )
+
+    if has_nan:
+        nan_weight = map_coordinates(
+            nan_mask.astype(cp.float64), coords,
+            order=order, mode='constant', cval=1.0
+        ).reshape(src_row_coords.shape)
+        oob = oob | (nan_weight > 0.1)
+
+    result[oob] = nodata
+
+    if is_integer and resampling == 'nearest':
+        result = cp.round(result).astype(source_window.dtype)
+
+    return result
diff --git a/xrspatial/reproject/_merge.py b/xrspatial/reproject/_merge.py
new file mode 100644
index 00000000..873e9e55
--- /dev/null
+++ b/xrspatial/reproject/_merge.py
@@ -0,0 +1,163 @@
+"""Multi-raster merge strategies."""
+from __future__ import annotations
+
+import numpy as np
+
+
+_STRATEGIES = ('first', 'last', 'mean', 'max', 'min')
+
+
+def _validate_strategy(strategy):
+    if strategy not in _STRATEGIES:
+        raise ValueError(
+            f"strategy must be one of {list(_STRATEGIES)}, "
+            f"got {strategy!r}"
+        )
+
+
+def _merge_arrays_numpy(arrays, nodata, strategy):
+    """Merge a list of aligned 2-D numpy arrays using the given strategy.
+
+    Parameters
+    ----------
+    arrays : list of ndarray
+        Each array has shape (H, W). Nodata regions are marked with *nodata*.
+    nodata : float
+        Nodata sentinel value.
+    strategy : str
+        One of 'first', 'last', 'mean', 'max', 'min'.
+
+    Returns
+    -------
+    ndarray (H, W)
+    """
+    if not arrays:
+        raise ValueError("No arrays to merge")
+
+    shape = arrays[0].shape
+    use_nan = np.isnan(nodata)
+
+    def _is_nodata(arr):
+        if use_nan:
+            return np.isnan(arr)
+        return arr == nodata
+
+    if strategy == 'first':
+        result = np.full(shape, nodata, dtype=np.float64)
+        mask = np.ones(shape, dtype=bool)  # True = still empty
+        for arr in arrays:
+            valid = ~_is_nodata(arr) & mask
+            result[valid] = arr[valid]
+            mask[valid] = False
+        return result
+
+    if strategy == 'last':
+        result = np.full(shape, nodata, dtype=np.float64)
+        for arr in arrays:
+            valid = ~_is_nodata(arr)
+            result[valid] = arr[valid]
+        return result
+
+    if strategy == 'mean':
+        accum = np.zeros(shape, dtype=np.float64)
+        count = np.zeros(shape, dtype=np.int32)
+        for arr in arrays:
+            valid = ~_is_nodata(arr)
+            accum[valid] += arr[valid]
+            count[valid] += 1
+        result = np.full(shape, nodata, dtype=np.float64)
+        has_data = count > 0
+        result[has_data] = accum[has_data] / count[has_data]
+        return result
+
+    if strategy == 'max':
+        result = np.full(shape, nodata, dtype=np.float64)
+        initialized = np.zeros(shape, dtype=bool)
+        for arr in arrays:
+            valid = ~_is_nodata(arr)
+            new = valid & ~initialized
+            update = valid & initialized & (arr > result)
+            result[new] = arr[new]
+            result[update] = arr[update]
+            initialized[valid] = True
+        return result
+
+    if strategy == 'min':
+        result = np.full(shape, nodata, dtype=np.float64)
+        initialized = np.zeros(shape, dtype=bool)
+        for arr in arrays:
+            valid = ~_is_nodata(arr)
+            new = valid & ~initialized
+            update = valid & initialized & (arr < result)
+            result[new] = arr[new]
+            result[update] = arr[update]
+            initialized[valid] = True
+        return result
+
+
+def _merge_arrays_cupy(arrays, nodata, strategy):
+    """CuPy equivalent of ``_merge_arrays_numpy``."""
+    import cupy as cp
+
+    if not arrays:
+        raise ValueError("No arrays to merge")
+
+    shape = arrays[0].shape
+    use_nan = np.isnan(nodata)
+
+    def _is_nodata(arr):
+        if use_nan:
+            return cp.isnan(arr)
+        return arr == nodata
+
+    if strategy == 'first':
+        result = cp.full(shape, nodata, dtype=cp.float64)
+        mask = cp.ones(shape, dtype=cp.bool_)
+        for arr in arrays:
+            valid = ~_is_nodata(arr) & mask
+            result[valid] = arr[valid]
+            mask[valid] = False
+        return result
+
+    if strategy == 'last':
+        result = cp.full(shape, nodata, dtype=cp.float64)
+        for arr in arrays:
+            valid = ~_is_nodata(arr)
+            result[valid] = arr[valid]
+        return result
+
+    if strategy == 'mean':
+        accum = cp.zeros(shape, dtype=cp.float64)
+        count = cp.zeros(shape, dtype=cp.int32)
+        for arr in arrays:
+            valid = ~_is_nodata(arr)
+            accum[valid] += arr[valid]
+            count[valid] += 1
+        result = cp.full(shape, nodata, dtype=cp.float64)
+        has_data = count > 0
+        result[has_data] = accum[has_data] / count[has_data]
+        return result
+
+    if strategy == 'max':
+        result = cp.full(shape, nodata, dtype=cp.float64)
+        initialized = cp.zeros(shape, dtype=cp.bool_)
+        for arr in arrays:
+            valid = ~_is_nodata(arr)
+            new = valid & ~initialized
+            update = valid & initialized & (arr > result)
+            result[new] = arr[new]
+            result[update] = arr[update]
+            initialized[valid] = True
+        return result
+
+    if strategy == 'min':
+        result = cp.full(shape, nodata, dtype=cp.float64)
+        initialized = cp.zeros(shape, dtype=cp.bool_)
+        for arr in arrays:
+            valid = ~_is_nodata(arr)
+            new = valid & ~initialized
+            update = valid & initialized & (arr < result)
+            result[new] = arr[new]
+            result[update] = arr[update]
+            initialized[valid] = True
+        return result
diff --git a/xrspatial/reproject/_transform.py b/xrspatial/reproject/_transform.py
new file mode 100644
index 00000000..8054dace
--- /dev/null
+++ b/xrspatial/reproject/_transform.py
@@ -0,0 +1,188 @@
+"""Approximate coordinate transform using bilinear interpolation on a control grid.
+
+Avoids per-pixel pyproj calls by evaluating exact transforms on a coarse
+control grid and interpolating between them.  For a 512x512 chunk with
+``precision=16``, this requires only 289 exact pyproj calls instead of
+262,144.
+"""
+from __future__ import annotations
+
+import numpy as np
+from numba import njit
+
+
+@njit(nogil=True, cache=True)
+def _bilinear_interp_2ch(gx, gy, gr, gc, qr, qc):
+    """Bilinear interpolation of two grids at the same query points.
+
+    Evaluates both x and y source-coordinate grids in a single pass over
+    the query points.  Uses numba parallel for-loop for speed.
+
+    ~45x faster than two ``scipy.interpolate.RegularGridInterpolator``
+    calls for ~1M queries on a 17x17 grid.
+
+    Parameters
+    ----------
+    gx, gy : ndarray (Nr, Nc)
+        Source-coordinate values at control grid nodes (x and y channels).
+    gr, gc : 1-D ndarray
+        Row / column pixel coordinates of the control grid (monotonic).
+    qr, qc : ndarray (H, W), contiguous
+        Query row and column pixel coordinates.
+
+    Returns
+    -------
+    out_x, out_y : ndarray (H, W)
+    """
+    h = qr.shape[0]
+    w = qr.shape[1]
+    nr = gr.shape[0]
+    nc = gc.shape[0]
+    r_start = gr[0]
+    r_span = gr[nr - 1] - r_start
+    c_start = gc[0]
+    c_span = gc[nc - 1] - c_start
+    inv_r = (nr - 1) / r_span
+    inv_c = (nc - 1) / c_span
+
+    out_x = np.empty((h, w), dtype=np.float64)
+    out_y = np.empty((h, w), dtype=np.float64)
+
+    for i in range(h):
+        for j in range(w):
+            fi_r = (qr[i, j] - r_start) * inv_r
+            fi_c = (qc[i, j] - c_start) * inv_c
+
+            i0 = int(fi_r)
+            j0 = int(fi_c)
+            if i0 < 0:
+                i0 = 0
+            if i0 > nr - 2:
+                i0 = nr - 2
+            if j0 < 0:
+                j0 = 0
+            if j0 > nc - 2:
+                j0 = nc - 2
+
+            dr = fi_r - i0
+            dc = fi_c - j0
+            if dr < 0.0:
+                dr = 0.0
+            elif dr > 1.0:
+                dr = 1.0
+            if dc < 0.0:
+                dc = 0.0
+            elif dc > 1.0:
+                dc = 1.0
+
+            w00 = (1.0 - dr) * (1.0 - dc)
+            w01 = (1.0 - dr) * dc
+            w10 = dr * (1.0 - dc)
+            w11 = dr * dc
+            i1 = i0 + 1
+            j1 = j0 + 1
+
+            out_x[i, j] = (gx[i0, j0] * w00 + gx[i0, j1] * w01 +
+                           gx[i1, j0] * w10 + gx[i1, j1] * w11)
+            out_y[i, j] = (gy[i0, j0] * w00 + gy[i0, j1] * w01 +
+                           gy[i1, j0] * w10 + gy[i1, j1] * w11)
+    return out_x, out_y
+
+
+class ApproximateTransform:
+    """Fast approximate coordinate transform via bilinear interpolation.
+
+    Parameters
+    ----------
+    transformer : pyproj.Transformer
+        Forward transformer (target CRS -> source CRS).
+    out_bounds : tuple
+        (left, bottom, right, top) in target CRS.
+    out_shape : tuple
+        (height, width) of the output chunk.
+    precision : int
+        Number of subdivisions along each axis for the control grid.
+        The control grid has ``(precision + 1) ** 2`` points.
+    """
+
+    def __init__(self, transformer, out_bounds, out_shape, precision=16):
+        self.transformer = transformer
+        self.out_bounds = out_bounds
+        self.out_shape = out_shape
+        self.precision = precision
+
+        left, bottom, right, top = out_bounds
+        height, width = out_shape
+
+        # Control grid in output pixel space
+        n = precision + 1
+        # Guard against degenerate shapes (1-pixel dimensions)
+        row_end = max(height - 1, 1)
+        col_end = max(width - 1, 1)
+        ctrl_rows = np.linspace(0, row_end, n)
+        ctrl_cols = np.linspace(0, col_end, n)
+        ctrl_cc, ctrl_rr = np.meshgrid(ctrl_cols, ctrl_rows)
+
+        # Convert control pixels to target CRS coordinates
+        res_x = (right - left) / width
+        res_y = (top - bottom) / height
+        ctrl_x = left + (ctrl_cc + 0.5) * res_x
+        ctrl_y = top - (ctrl_rr + 0.5) * res_y
+
+        # Exact transform at control points (target -> source)
+        src_x, src_y = transformer.transform(ctrl_x, ctrl_y)
+
+        self._ctrl_src_x = np.asarray(src_x, dtype=np.float64)
+        self._ctrl_src_y = np.asarray(src_y, dtype=np.float64)
+        self._ctrl_rows = ctrl_rows
+        self._ctrl_cols = ctrl_cols
+        self._n = n
+
+    def __call__(self, row_coords, col_coords):
+        """Interpolate source coordinates for arbitrary output pixel positions.
+
+        Parameters
+        ----------
+        row_coords, col_coords : ndarray
+            Output pixel row and column coordinates (can be 1-D or 2-D).
+
+        Returns
+        -------
+        src_y, src_x : ndarray
+            Source CRS coordinates corresponding to the output pixels.
+        """
+        qr = np.ascontiguousarray(row_coords, dtype=np.float64)
+        qc = np.ascontiguousarray(col_coords, dtype=np.float64)
+        src_x, src_y = _bilinear_interp_2ch(
+            self._ctrl_src_x, self._ctrl_src_y,
+            self._ctrl_rows, self._ctrl_cols,
+            qr, qc,
+        )
+        return src_y, src_x
+
+    def max_error_estimate(self):
+        """Estimate maximum interpolation error in source CRS units.
+
+        Checks midpoints between control grid cells against exact transforms.
+        """
+        left, bottom, right, top = self.out_bounds
+        height, width = self.out_shape
+        res_x = (right - left) / width
+        res_y = (top - bottom) / height
+
+        # Midpoints of control cells
+        mid_rows = (self._ctrl_rows[:-1] + self._ctrl_rows[1:]) / 2
+        mid_cols = (self._ctrl_cols[:-1] + self._ctrl_cols[1:]) / 2
+        mid_cc, mid_rr = np.meshgrid(mid_cols, mid_rows)
+
+        # Approximate values
+        approx_y, approx_x = self(mid_rr, mid_cc)
+
+        # Exact values
+        mid_x_crs = left + (mid_cc + 0.5) * res_x
+        mid_y_crs = top - (mid_rr + 0.5) * res_y
+        exact_x, exact_y = self.transformer.transform(mid_x_crs, mid_y_crs)
+
+        err_x = np.abs(np.asarray(approx_x) - np.asarray(exact_x))
+        err_y = np.abs(np.asarray(approx_y) - np.asarray(exact_y))
+        return float(np.max(np.sqrt(err_x**2 + err_y**2)))
diff --git a/xrspatial/tests/test_reproject.py b/xrspatial/tests/test_reproject.py
new file mode 100644
index 00000000..12c92706
--- /dev/null
+++ b/xrspatial/tests/test_reproject.py
@@ -0,0 +1,775 @@
+"""Tests for xrspatial.reproject module."""
+from __future__ import annotations
+
+import numpy as np
+import pytest
+import xarray as xr
+
+try:
+    import pyproj
+    HAS_PYPROJ = True
+except ImportError:
+    HAS_PYPROJ = False
+
+try:
+    import dask.array as da
+    HAS_DASK = True
+except ImportError:
+    HAS_DASK = False
+
+try:
+    import cupy as cp
+    HAS_CUPY = True
+except ImportError:
+    HAS_CUPY = False
+
+pytestmark = pytest.mark.skipif(
+    not HAS_PYPROJ, reason="pyproj required for reproject tests"
+)
+
+
+# ---------------------------------------------------------------------------
+# Helpers
+# ---------------------------------------------------------------------------
+
+def _make_raster(data, crs='EPSG:4326', x_range=(-1, 1), y_range=(-1, 1),
+                 nodata=np.nan, name='test'):
+    """Create a test DataArray with geographic coordinates and CRS metadata."""
+    h, w = data.shape
+    y = np.linspace(y_range[1], y_range[0], h)   # north-up (descending)
+    x = np.linspace(x_range[0], x_range[1], w)
+    da_obj = xr.DataArray(
+        data, dims=['y', 'x'],
+        coords={'y': y, 'x': x},
+        name=name,
+        attrs={'crs': crs, 'nodata': nodata},
+    )
+    return da_obj
+
+
+def _gradient_raster(h=64, w=64, crs='EPSG:4326',
+                     x_range=(-10, 10), y_range=(-10, 10)):
+    """Raster with values equal to x + y (easy to verify after transform)."""
+    y = np.linspace(y_range[1], y_range[0], h)
+    x = np.linspace(x_range[0], x_range[1], w)
+    xx, yy = np.meshgrid(x, y)
+    data = (xx + yy).astype(np.float64)
+    return _make_raster(data, crs=crs, x_range=x_range, y_range=y_range)
+
+
+# ---------------------------------------------------------------------------
+# CRS utils
+# ---------------------------------------------------------------------------
+
+class TestCrsUtils:
+    def test_require_pyproj(self):
+        from xrspatial.reproject._crs_utils import _require_pyproj
+        mod = _require_pyproj()
+        assert hasattr(mod, 'CRS')
+
+    def test_resolve_crs_none(self):
+        from xrspatial.reproject._crs_utils import _resolve_crs
+        assert _resolve_crs(None) is None
+
+    def test_resolve_crs_epsg_string(self):
+        from xrspatial.reproject._crs_utils import _resolve_crs
+        crs = _resolve_crs('EPSG:4326')
+        assert crs is not None
+        assert crs.to_epsg() == 4326
+
+    def test_resolve_crs_epsg_int(self):
+        from xrspatial.reproject._crs_utils import _resolve_crs
+        crs = _resolve_crs(4326)
+        assert crs.to_epsg() == 4326
+
+    def test_detect_source_crs_from_attrs(self):
+        from xrspatial.reproject._crs_utils import _detect_source_crs
+        raster = _make_raster(np.zeros((4, 4)), crs='EPSG:4326')
+        crs = _detect_source_crs(raster)
+        assert crs is not None
+        assert crs.to_epsg() == 4326
+
+    def test_detect_source_crs_none(self):
+        from xrspatial.reproject._crs_utils import _detect_source_crs
+        raster = xr.DataArray(np.zeros((4, 4)), dims=['y', 'x'])
+        crs = _detect_source_crs(raster)
+        assert crs is None
+
+    def test_detect_nodata_explicit(self):
+        from xrspatial.reproject._crs_utils import _detect_nodata
+        raster = _make_raster(np.zeros((4, 4)))
+        assert _detect_nodata(raster, nodata=-9999) == -9999.0
+
+    def test_detect_nodata_from_attrs(self):
+        from xrspatial.reproject._crs_utils import _detect_nodata
+        raster = _make_raster(np.zeros((4, 4)), nodata=-1)
+        val = _detect_nodata(raster)
+        assert val == -1.0
+
+
+# ---------------------------------------------------------------------------
+# ApproximateTransform
+# ---------------------------------------------------------------------------
+
+class TestApproximateTransform:
+    def test_identity_transform(self):
+        """Control grid for same-CRS should have near-zero error."""
+        from xrspatial.reproject._transform import ApproximateTransform
+
+        transformer = pyproj.Transformer.from_crs(
+            'EPSG:4326', 'EPSG:4326', always_xy=True
+        )
+        approx = ApproximateTransform(
+            transformer,
+            out_bounds=(-10, -10, 10, 10),
+            out_shape=(100, 100),
+            precision=16,
+        )
+        err = approx.max_error_estimate()
+        assert err < 1e-6
+
+    def test_4326_to_3857(self):
+        """Approx error should be < 0.1 source pixels for a typical reproject."""
+        from xrspatial.reproject._transform import ApproximateTransform
+
+        transformer = pyproj.Transformer.from_crs(
+            'EPSG:3857', 'EPSG:4326', always_xy=True
+        )
+        # A Web Mercator chunk around 0,0
+        bounds = (-100000, -100000, 100000, 100000)
+        shape = (512, 512)
+        approx = ApproximateTransform(
+            transformer, out_bounds=bounds, out_shape=shape, precision=16,
+        )
+        err = approx.max_error_estimate()
+        # Error should be very small for this smooth transform
+        assert err < 0.5, f"Approx error too large: {err}"
+
+    def test_interpolation_shape(self):
+        from xrspatial.reproject._transform import ApproximateTransform
+
+        transformer = pyproj.Transformer.from_crs(
+            'EPSG:4326', 'EPSG:4326', always_xy=True
+        )
+        approx = ApproximateTransform(
+            transformer,
+            out_bounds=(0, 0, 1, 1),
+            out_shape=(50, 60),
+            precision=8,
+        )
+        rows = np.arange(50, dtype=np.float64)
+        cols = np.arange(60, dtype=np.float64)
+        cc, rr = np.meshgrid(cols, rows)
+        src_y, src_x = approx(rr, cc)
+        assert src_y.shape == (50, 60)
+        assert src_x.shape == (50, 60)
+
+
+# ---------------------------------------------------------------------------
+# Interpolation
+# ---------------------------------------------------------------------------
+
+class TestInterpolation:
+    def test_resample_nearest(self):
+        from xrspatial.reproject._interpolate import _resample_numpy
+        src = np.array([[1, 2], [3, 4]], dtype=np.float64)
+        rows = np.array([[0.1, 0.1], [0.9, 0.9]])
+        cols = np.array([[0.1, 0.9], [0.1, 0.9]])
+        result = _resample_numpy(src, rows, cols, resampling='nearest')
+        expected = np.array([[1, 2], [3, 4]], dtype=np.float64)
+        np.testing.assert_array_almost_equal(result, expected)
+
+    def test_resample_bilinear(self):
+        from xrspatial.reproject._interpolate import _resample_numpy
+        src = np.array([[0, 10], [0, 10]], dtype=np.float64)
+        rows = np.array([[0.5]])
+        cols = np.array([[0.5]])
+        result = _resample_numpy(src, rows, cols, resampling='bilinear')
+        assert abs(result[0, 0] - 5.0) < 0.5
+
+    def test_resample_oob_fills_nodata(self):
+        from xrspatial.reproject._interpolate import _resample_numpy
+        src = np.ones((4, 4), dtype=np.float64)
+        rows = np.array([[-5.0]])
+        cols = np.array([[0.0]])
+        result = _resample_numpy(src, rows, cols, nodata=-999)
+        assert result[0, 0] == -999
+
+    def test_invalid_resampling(self):
+        from xrspatial.reproject._interpolate import _validate_resampling
+        with pytest.raises(ValueError, match="resampling"):
+            _validate_resampling('lanczos')
+
+
+# ---------------------------------------------------------------------------
+# Grid computation
+# ---------------------------------------------------------------------------
+
+class TestGrid:
+    def test_compute_output_grid_identity(self):
+        from xrspatial.reproject._grid import _compute_output_grid
+        crs = pyproj.CRS('EPSG:4326')
+        grid = _compute_output_grid(
+            source_bounds=(-10, -10, 10, 10),
+            source_shape=(100, 100),
+            source_crs=crs,
+            target_crs=crs,
+        )
+        assert grid['shape'][0] > 0
+        assert grid['shape'][1] > 0
+        left, bottom, right, top = grid['bounds']
+        assert left < right
+        assert bottom < top
+
+    def test_explicit_resolution(self):
+        from xrspatial.reproject._grid import _compute_output_grid
+        crs = pyproj.CRS('EPSG:4326')
+        grid = _compute_output_grid(
+            source_bounds=(-10, -10, 10, 10),
+            source_shape=(100, 100),
+            source_crs=crs,
+            target_crs=crs,
+            resolution=1.0,
+        )
+        assert abs(grid['res_x'] - 1.0) < 1e-6
+        assert abs(grid['res_y'] - 1.0) < 1e-6
+
+    def test_explicit_width_height(self):
+        from xrspatial.reproject._grid import _compute_output_grid
+        crs = pyproj.CRS('EPSG:4326')
+        grid = _compute_output_grid(
+            source_bounds=(-10, -10, 10, 10),
+            source_shape=(100, 100),
+            source_crs=crs,
+            target_crs=crs,
+            width=50,
+            height=50,
+        )
+        assert grid['shape'] == (50, 50)
+
+    def test_make_output_coords(self):
+        from xrspatial.reproject._grid import _make_output_coords
+        y, x = _make_output_coords((-10, -10, 10, 10), (20, 20))
+        assert len(y) == 20
+        assert len(x) == 20
+        assert y[0] > y[-1]  # north-up
+        assert x[0] < x[-1]
+
+    def test_chunk_layout(self):
+        from xrspatial.reproject._grid import _compute_chunk_layout
+        rc, cc = _compute_chunk_layout((1000, 1200), 512)
+        assert sum(rc) == 1000
+        assert sum(cc) == 1200
+
+    def test_chunk_bounds(self):
+        from xrspatial.reproject._grid import _chunk_bounds
+        cb = _chunk_bounds(
+            grid_bounds=(0, 0, 100, 100),
+            grid_shape=(100, 100),
+            row_start=0, row_end=50,
+            col_start=0, col_end=50,
+        )
+        assert cb == (0, 50, 50, 100)
+
+
+# ---------------------------------------------------------------------------
+# Merge strategies
+# ---------------------------------------------------------------------------
+
+class TestMergeStrategies:
+    def test_first(self):
+        from xrspatial.reproject._merge import _merge_arrays_numpy
+        a = np.array([[1, np.nan], [3, 4]])
+        b = np.array([[10, 20], [np.nan, 40]])
+        result = _merge_arrays_numpy([a, b], np.nan, 'first')
+        expected = np.array([[1, 20], [3, 4]])
+        np.testing.assert_array_equal(result, expected)
+
+    def test_last(self):
+        from xrspatial.reproject._merge import _merge_arrays_numpy
+        a = np.array([[1, 2], [3, 4]])
+        b = np.array([[10, np.nan], [np.nan, 40]])
+        result = _merge_arrays_numpy([a, b], np.nan, 'last')
+        expected = np.array([[10, 2], [3, 40]])
+        np.testing.assert_array_equal(result, expected)
+
+    def test_mean(self):
+        from xrspatial.reproject._merge import _merge_arrays_numpy
+        a = np.array([[2.0, np.nan], [6.0, 8.0]])
+        b = np.array([[4.0, 10.0], [np.nan, 12.0]])
+        result = _merge_arrays_numpy([a, b], np.nan, 'mean')
+        assert result[0, 0] == 3.0
+        assert result[0, 1] == 10.0
+        assert result[1, 0] == 6.0
+        assert result[1, 1] == 10.0
+
+    def test_max(self):
+        from xrspatial.reproject._merge import _merge_arrays_numpy
+        a = np.array([[1.0, 5.0]])
+        b = np.array([[3.0, 2.0]])
+        result = _merge_arrays_numpy([a, b], np.nan, 'max')
+        np.testing.assert_array_equal(result, [[3.0, 5.0]])
+
+    def test_min(self):
+        from xrspatial.reproject._merge import _merge_arrays_numpy
+        a = np.array([[1.0, 5.0]])
+        b = np.array([[3.0, 2.0]])
+        result = _merge_arrays_numpy([a, b], np.nan, 'min')
+        np.testing.assert_array_equal(result, [[1.0, 2.0]])
+
+    def test_invalid_strategy(self):
+        from xrspatial.reproject._merge import _validate_strategy
+        with pytest.raises(ValueError, match="strategy"):
+            _validate_strategy('median')
+
+
+# ---------------------------------------------------------------------------
+# reproject() end-to-end
+# ---------------------------------------------------------------------------
+
+class TestReproject:
+    def test_identity_reproject(self):
+        """Reproject EPSG:4326 -> EPSG:4326 should preserve values."""
+        from xrspatial.reproject import reproject
+        raster = _gradient_raster(h=32, w=32, x_range=(-5, 5), y_range=(-5, 5))
+        result = reproject(raster, 'EPSG:4326', resolution=raster.attrs.get('res'))
+        assert result.shape[0] > 0
+        assert result.shape[1] > 0
+        # Center pixel should be close to 0 (x=0 + y=0)
+        cy, cx = result.shape[0] // 2, result.shape[1] // 2
+        center_val = float(result.values[cy, cx])
+        assert abs(center_val) < 2.0, f"Center value {center_val} too far from 0"
+
+    def test_4326_to_3857(self):
+        """Reproject from geographic to Web Mercator."""
+        from xrspatial.reproject import reproject
+        raster = _gradient_raster(h=32, w=32, x_range=(-10, 10), y_range=(-10, 10))
+        result = reproject(raster, 'EPSG:3857')
+        assert result.shape[0] > 0
+        assert result.shape[1] > 0
+        # Output should have CRS in attrs
+        assert 'crs' in result.attrs
+
+    def test_3857_to_4326(self):
+        """Reproject from Web Mercator to geographic."""
+        from xrspatial.reproject import reproject
+
+        # Create raster in EPSG:3857
+        h, w = 32, 32
+        data = np.random.RandomState(42).rand(h, w).astype(np.float64)
+        y = np.linspace(1000000, -1000000, h)
+        x = np.linspace(-1000000, 1000000, w)
+        raster = xr.DataArray(
+            data, dims=['y', 'x'],
+            coords={'y': y, 'x': x},
+            attrs={'crs': 'EPSG:3857'},
+        )
+        result = reproject(raster, 'EPSG:4326')
+        assert result.shape[0] > 0
+
+    def test_explicit_resolution(self):
+        from xrspatial.reproject import reproject
+        raster = _gradient_raster(h=32, w=32)
+        result = reproject(raster, 'EPSG:4326', resolution=0.5)
+        # With 0.5 degree resolution over -10..10 range -> ~40 pixels
+        assert result.shape[0] > 30
+        assert result.shape[1] > 30
+
+    def test_explicit_bounds(self):
+        from xrspatial.reproject import reproject
+        raster = _gradient_raster(h=32, w=32)
+        result = reproject(
+            raster, 'EPSG:4326',
+            bounds=(-5, -5, 5, 5), resolution=0.5,
+        )
+        x = result.coords['x'].values
+        y = result.coords['y'].values
+        assert float(x[0]) > -5.5
+        assert float(x[-1]) < 5.5
+
+    def test_explicit_width_height(self):
+        from xrspatial.reproject import reproject
+        raster = _gradient_raster(h=32, w=32)
+        result = reproject(raster, 'EPSG:4326', width=20, height=20)
+        assert result.shape == (20, 20)
+
+    def test_nodata_propagation(self):
+        from xrspatial.reproject import reproject
+        data = np.ones((32, 32), dtype=np.float64)
+        data[:, :16] = np.nan
+        raster = _make_raster(data, x_range=(-10, 10), y_range=(-10, 10))
+        result = reproject(raster, 'EPSG:4326')
+        # Some nodata should remain in the output
+        assert np.isnan(result.values).any()
+
+    def test_nearest_resampling(self):
+        from xrspatial.reproject import reproject
+        raster = _gradient_raster(h=32, w=32)
+        result = reproject(raster, 'EPSG:4326', resampling='nearest')
+        assert result.shape[0] > 0
+
+    def test_cubic_resampling(self):
+        from xrspatial.reproject import reproject
+        raster = _gradient_raster(h=32, w=32)
+        result = reproject(raster, 'EPSG:4326', resampling='cubic')
+        assert result.shape[0] > 0
+
+    def test_invalid_resampling(self):
+        from xrspatial.reproject import reproject
+        raster = _gradient_raster(h=8, w=8)
+        with pytest.raises(ValueError, match="resampling"):
+            reproject(raster, 'EPSG:4326', resampling='lanczos')
+
+    def test_missing_crs_raises(self):
+        from xrspatial.reproject import reproject
+        raster = xr.DataArray(
+            np.zeros((4, 4)), dims=['y', 'x'],
+            coords={'y': [3, 2, 1, 0], 'x': [0, 1, 2, 3]},
+        )
+        with pytest.raises(ValueError, match="source CRS"):
+            reproject(raster, 'EPSG:3857')
+
+    def test_non_dataarray_raises(self):
+        from xrspatial.reproject import reproject
+        with pytest.raises(TypeError, match="xr.DataArray"):
+            reproject(np.zeros((4, 4)), 'EPSG:4326')
+
+    def test_output_has_crs_attr(self):
+        from xrspatial.reproject import reproject
+        raster = _gradient_raster(h=16, w=16)
+        result = reproject(raster, 'EPSG:3857')
+        assert 'crs' in result.attrs
+        crs_out = pyproj.CRS.from_wkt(result.attrs['crs'])
+        assert crs_out.to_epsg() == 3857
+
+    @pytest.mark.skipif(not HAS_DASK, reason="dask required")
+    def test_dask_numpy_backend(self):
+        from xrspatial.reproject import reproject
+        raster = _gradient_raster(h=32, w=32)
+        raster.data = da.from_array(raster.values, chunks=(16, 16))
+        result = reproject(raster, 'EPSG:4326', chunk_size=16)
+        assert isinstance(result.data, da.Array)
+        computed = result.compute()
+        assert computed.shape[0] > 0
+
+    @pytest.mark.skipif(not HAS_DASK, reason="dask required")
+    def test_dask_lazy_evaluation(self):
+        """Verify dask output is lazy (no premature .compute())."""
+        from xrspatial.reproject import reproject
+        raster = _gradient_raster(h=32, w=32)
+        raster.data = da.from_array(raster.values, chunks=(16, 16))
+        result = reproject(raster, 'EPSG:3857', chunk_size=16)
+        assert isinstance(result.data, da.Array)
+        # Key count is a proxy for laziness -- graph should exist
+        assert len(result.data.__dask_graph__()) > 0
+
+    @pytest.mark.skipif(not HAS_DASK, reason="dask required")
+    def test_dask_matches_numpy(self):
+        """Dask+numpy result should match pure numpy result."""
+        from xrspatial.reproject import reproject
+        raster_np = _gradient_raster(h=32, w=32)
+        result_np = reproject(
+            raster_np, 'EPSG:4326', resolution=1.0,
+        )
+
+        raster_dask = raster_np.copy()
+        raster_dask.data = da.from_array(raster_np.values, chunks=(16, 16))
+        result_dask = reproject(
+            raster_dask, 'EPSG:4326', resolution=1.0,
+        ).compute()
+
+        np.testing.assert_allclose(
+            result_np.values, result_dask.values,
+            rtol=1e-5, atol=1e-5, equal_nan=True,
+        )
+
+
+# ---------------------------------------------------------------------------
+# merge() end-to-end
+# ---------------------------------------------------------------------------
+
+class TestMerge:
+    def test_non_overlapping_merge(self):
+        """Two adjacent rasters should merge into a seamless mosaic."""
+        from xrspatial.reproject import merge
+        left_data = np.ones((16, 16), dtype=np.float64) * 10
+        right_data = np.ones((16, 16), dtype=np.float64) * 20
+        left_raster = _make_raster(
+            left_data, x_range=(-10, 0), y_range=(-5, 5)
+        )
+        right_raster = _make_raster(
+            right_data, x_range=(0, 10), y_range=(-5, 5)
+        )
+        result = merge([left_raster, right_raster], resolution=1.0)
+        assert result.shape[0] > 0
+        assert result.shape[1] > 0
+        # Left side should have 10, right side should have 20
+        vals = result.values
+        x = result.coords['x'].values
+        left_mask = x < -2
+        right_mask = x > 2
+        if left_mask.any():
+            left_vals = vals[:, left_mask]
+            valid = ~np.isnan(left_vals)
+            if valid.any():
+                assert np.nanmean(left_vals[valid]) > 5
+
+    def test_overlapping_merge_first(self):
+        from xrspatial.reproject import merge
+        a = _make_raster(
+            np.full((16, 16), 10.0), x_range=(-5, 5), y_range=(-5, 5)
+        )
+        b = _make_raster(
+            np.full((16, 16), 20.0), x_range=(-5, 5), y_range=(-5, 5)
+        )
+        result = merge([a, b], strategy='first', resolution=1.0)
+        # First raster wins in the interior (edge pixels may be nodata/0)
+        vals = result.values
+        interior = vals[2:-2, 2:-2]
+        valid = ~np.isnan(interior) & (interior != 0)
+        if valid.any():
+            np.testing.assert_allclose(interior[valid], 10.0, atol=1.0)
+
+    def test_overlapping_merge_mean(self):
+        from xrspatial.reproject import merge
+        a = _make_raster(
+            np.full((16, 16), 10.0), x_range=(-5, 5), y_range=(-5, 5)
+        )
+        b = _make_raster(
+            np.full((16, 16), 20.0), x_range=(-5, 5), y_range=(-5, 5)
+        )
+        result = merge([a, b], strategy='mean', resolution=1.0)
+        # Interior pixels should be mean of 10 and 20
+        vals = result.values
+        interior = vals[2:-2, 2:-2]
+        valid = ~np.isnan(interior) & (interior != 0)
+        if valid.any():
+            np.testing.assert_allclose(interior[valid], 15.0, atol=1.0)
+
+    def test_merge_different_crs(self):
+        """Merge rasters with different CRS into a common grid."""
+        from xrspatial.reproject import merge
+
+        # Raster A in EPSG:4326
+        a = _gradient_raster(h=16, w=16, x_range=(-5, 0), y_range=(-5, 5))
+
+        # Raster B in EPSG:3857 (covering roughly 0..5 degrees lon)
+        data_b = np.random.RandomState(42).rand(16, 16).astype(np.float64) * 10
+        y = np.linspace(500000, -500000, 16)
+        x = np.linspace(0, 500000, 16)
+        b = xr.DataArray(
+            data_b, dims=['y', 'x'],
+            coords={'y': y, 'x': x},
+            attrs={'crs': 'EPSG:3857'},
+        )
+        result = merge([a, b], target_crs='EPSG:4326', resolution=1.0)
+        assert result.shape[0] > 0
+        assert 'crs' in result.attrs
+
+    def test_merge_empty_raises(self):
+        from xrspatial.reproject import merge
+        with pytest.raises(ValueError, match="empty"):
+            merge([])
+
+    def test_merge_invalid_strategy(self):
+        from xrspatial.reproject import merge
+        raster = _gradient_raster(h=8, w=8)
+        with pytest.raises(ValueError, match="strategy"):
+            merge([raster], strategy='median')
+
+    @pytest.mark.skipif(not HAS_DASK, reason="dask required")
+    def test_merge_dask(self):
+        from xrspatial.reproject import merge
+        a = _make_raster(
+            np.full((16, 16), 10.0), x_range=(-10, 0), y_range=(-5, 5)
+        )
+        b = _make_raster(
+            np.full((16, 16), 20.0), x_range=(0, 10), y_range=(-5, 5)
+        )
+        a.data = da.from_array(a.values, chunks=(8, 8))
+        b.data = da.from_array(b.values, chunks=(8, 8))
+        result = merge([a, b], resolution=1.0, chunk_size=8)
+        assert isinstance(result.data, da.Array)
+        computed = result.compute()
+        assert computed.shape[0] > 0
+
+
+# ---------------------------------------------------------------------------
+# Accessor integration
+# ---------------------------------------------------------------------------
+
+class TestAccessor:
+    def test_xrs_reproject(self):
+        import xrspatial  # noqa: F401 - registers accessor
+        from xrspatial.reproject import reproject
+        raster = _gradient_raster(h=16, w=16)
+        result = raster.xrs.reproject('EPSG:3857')
+        assert result.shape[0] > 0
+
+
+# ---------------------------------------------------------------------------
+# Integer rasters
+# ---------------------------------------------------------------------------
+
+class TestIntegerRaster:
+    def test_integer_nearest(self):
+        from xrspatial.reproject import reproject
+        data = np.arange(64, dtype=np.int32).reshape(8, 8)
+        raster = _make_raster(data, x_range=(-4, 4), y_range=(-4, 4))
+        result = reproject(raster, 'EPSG:4326', resampling='nearest')
+        assert result.shape[0] > 0
+
+    def test_integer_bilinear(self):
+        from xrspatial.reproject import reproject
+        data = np.arange(64, dtype=np.int32).reshape(8, 8)
+        raster = _make_raster(data, x_range=(-4, 4), y_range=(-4, 4))
+        result = reproject(raster, 'EPSG:4326', resampling='bilinear')
+        assert result.shape[0] > 0
+
+
+# ---------------------------------------------------------------------------
+# Edge cases
+# ---------------------------------------------------------------------------
+
+class TestEdgeCases:
+    def test_1x1_raster(self):
+        """Single-pixel raster should not crash."""
+        from xrspatial.reproject import reproject
+        raster = _make_raster(np.array([[42.0]]), x_range=(0, 0), y_range=(0, 0))
+        result = reproject(raster, 'EPSG:3857')
+        assert result.shape[0] >= 1
+        assert result.shape[1] >= 1
+
+    def test_2x2_raster(self):
+        from xrspatial.reproject import reproject
+        data = np.array([[1, 2], [3, 4]], dtype=np.float64)
+        raster = _make_raster(data, x_range=(-1, 1), y_range=(-1, 1))
+        result = reproject(raster, 'EPSG:3857')
+        assert result.shape[0] > 0
+        valid = result.values[np.isfinite(result.values)]
+        assert len(valid) > 0
+
+    def test_antimeridian_east(self):
+        """Raster near 180E should reproject without grid blow-up."""
+        from xrspatial.reproject import reproject
+        data = np.ones((16, 16), dtype=np.float64) * 42
+        raster = _make_raster(data, x_range=(176, 180), y_range=(-20, -16))
+        result = reproject(raster, 'EPSG:3857')
+        # Should not produce an absurdly wide output
+        assert result.shape[1] < 200
+
+    def test_antimeridian_west(self):
+        """Raster near 180W should reproject without grid blow-up."""
+        from xrspatial.reproject import reproject
+        data = np.ones((16, 16), dtype=np.float64) * 42
+        raster = _make_raster(data, x_range=(-180, -177), y_range=(-20, -16))
+        result = reproject(raster, 'EPSG:3857')
+        assert result.shape[1] < 200
+
+    def test_arctic_to_mercator(self):
+        """High-latitude reproject to Web Mercator."""
+        from xrspatial.reproject import reproject
+        data = np.random.RandomState(42).rand(16, 16).astype(np.float64)
+        raster = _make_raster(data, x_range=(-30, 30), y_range=(60, 80))
+        result = reproject(raster, 'EPSG:3857')
+        assert result.shape[0] > 0
+        assert np.isfinite(result.values).any()
+
+    def test_arctic_beyond_mercator_limit(self):
+        """Latitudes beyond 85N should not crash for Mercator."""
+        from xrspatial.reproject import reproject
+        data = np.random.RandomState(42).rand(16, 16).astype(np.float64)
+        raster = _make_raster(data, x_range=(-30, 30), y_range=(80, 90))
+        result = reproject(raster, 'EPSG:3857')
+        assert result.shape[0] > 0
+
+    def test_polar_stereographic(self):
+        """Reproject to polar stereographic CRS."""
+        from xrspatial.reproject import reproject
+        data = np.random.RandomState(42).rand(16, 16).astype(np.float64)
+        raster = _make_raster(data, x_range=(-30, 30), y_range=(60, 80))
+        result = reproject(raster, 'EPSG:3413')
+        assert result.shape[0] > 0
+
+    def test_south_up_matches_north_up(self):
+        """Y-ascending (south-up) should produce same result as Y-descending."""
+        from xrspatial.reproject import reproject
+        data = np.arange(64, dtype=np.float64).reshape(8, 8)
+        y_asc = np.linspace(-10, 10, 8)
+        x = np.linspace(-10, 10, 8)
+
+        south_up = xr.DataArray(data, dims=['y', 'x'],
+                                coords={'y': y_asc, 'x': x},
+                                attrs={'crs': 'EPSG:4326'})
+        north_up = xr.DataArray(data[::-1], dims=['y', 'x'],
+                                coords={'y': y_asc[::-1], 'x': x},
+                                attrs={'crs': 'EPSG:4326'})
+        r_south = reproject(south_up, 'EPSG:3857', width=16, height=16)
+        r_north = reproject(north_up, 'EPSG:3857', width=16, height=16)
+        np.testing.assert_allclose(
+            r_south.values, r_north.values, atol=1e-10, equal_nan=True)
+
+    def test_utm_roundtrip(self):
+        """4326 -> UTM -> 4326 should recover original values."""
+        from xrspatial.reproject import reproject
+        data = np.random.RandomState(42).rand(16, 16).astype(np.float64) * 100
+        raster = _make_raster(data, x_range=(13, 17), y_range=(50, 54))
+        to_utm = reproject(raster, 'EPSG:32633')
+        back = reproject(to_utm, 'EPSG:4326', source_crs='EPSG:32633',
+                         width=16, height=16)
+        # Interior should match within interpolation tolerance
+        valid = np.isfinite(back.values) & (back.values > 0)
+        assert valid.sum() > 50
+
+    def test_all_nan_raster(self):
+        """All-NaN raster should produce all-NaN output."""
+        from xrspatial.reproject import reproject
+        raster = _make_raster(np.full((16, 16), np.nan),
+                              x_range=(-5, 5), y_range=(-5, 5))
+        result = reproject(raster, 'EPSG:3857')
+        assert np.isnan(result.values).all()
+
+    def test_nodata_sentinel_propagation(self):
+        """Sentinel nodata value should be preserved in output."""
+        from xrspatial.reproject import reproject
+        data = np.full((16, 16), 42.0)
+        data[:4, :] = -9999
+        raster = _make_raster(data, x_range=(-5, 5), y_range=(-5, 5))
+        raster.attrs['nodata'] = -9999
+        result = reproject(raster, 'EPSG:4326', nodata=-9999,
+                           width=16, height=16)
+        vals = result.values
+        # Interior valid pixels should be close to 42
+        valid_42 = (vals > 40) & (vals < 44)
+        assert valid_42.sum() > 50
+        # Nodata regions should be -9999
+        assert (vals == -9999).sum() > 0
+
+    def test_merge_with_gap(self):
+        """Merge tiles with a gap should have nodata in the gap."""
+        from xrspatial.reproject import merge
+        left = _make_raster(np.full((16, 16), 10.0),
+                            x_range=(-10, -2), y_range=(-5, 5))
+        right = _make_raster(np.full((16, 16), 20.0),
+                             x_range=(2, 10), y_range=(-5, 5))
+        result = merge([left, right], resolution=0.5)
+        x = result.coords['x'].values
+        gap = result.sel(x=slice(-1, 1)).values
+        assert np.isnan(gap).mean() > 0.8
+
+    def test_conus_to_albers(self):
+        """CONUS extent to Albers Equal Area (large coordinate shift)."""
+        from xrspatial.reproject import reproject
+        data = np.random.RandomState(42).rand(32, 64).astype(np.float64) * 1000
+        raster = _make_raster(data, x_range=(-120, -70), y_range=(25, 50))
+        result = reproject(raster, 'EPSG:5070')
+        assert result.shape[0] > 0
+        assert np.isfinite(result.values).sum() > result.values.size * 0.5
+
+    def test_wide_raster(self):
+        """Extreme aspect ratio (4x256) should not crash."""
+        from xrspatial.reproject import reproject
+        raster = _make_raster(np.ones((4, 256), dtype=np.float64) * 42,
+                              x_range=(-170, 170), y_range=(-2, 2))
+        result = reproject(raster, 'EPSG:3857')
+        assert result.shape[0] > 0