First pass at docs landing page redesign

docs/gers/gers-tutorial.mdx

@@ -18,7 +18,7 @@ To associate your data with GERS identifiers, we need to:
 
 Joining geospatial datasets is a highly context dependent task. Sometimes you want very precise matches, with little risk of false positives. And other times you want greedy, loose matches, with wide coverage. To demonstrate this context dependency, we’ll be adopting the following use case to frame our decisions:
 
-*We’re building a hyperlocal mobile application that allows people to browse restaurants in Alameda County. Users can search for, browse, bookmark, and share restaurants. We will be using [Overture’s Places theme](https://docs.overturemaps.org/guides/places/) as our foundational dataset, but we want to bring in additional datasets to add details to our restaurant listings.*
+*We’re building a hyperlocal mobile application that allows people to browse restaurants in Alameda County. Users can search for, browse, bookmark, and share restaurants. We will be using [Overture’s Places theme](../../guides/places) as our foundational dataset, but we want to bring in additional datasets to add details to our restaurant listings.*
 
 One dataset we want to add is [Alameda County’s restaurant inspection reports](https://data.acgov.org/datasets/e95ff2829e9d4ea0b3d8266aac37ff14_0/explore), a dataset detailing the health and safety scores for each restaurant. This data will allow our users to filter restaurants by their health scores and present these health scores in our restaurant profiles.
 
@@ -130,7 +130,7 @@ Using these common properties, and a bit of SQL, we can spin up a quick matcher
 
 ### Staging our datasets
 
-With our inspection data saved as `inspection_records.geojson`, let’s boot up [DuckDB](/getting-data/duckdb), specifying a local database as a cache.
+With our inspection data saved as `inspection_records.geojson`, let’s boot up [DuckDB](/getting-data/duckdb/), specifying a local database as a cache.
 
 ```bash
 $ duckdb inspection_match.ddb

docs/gers/index.mdx

@@ -18,10 +18,10 @@ The "S" in GERS stands for a *system*, with the following components:
 
 | Component | Description |
 | ---------- | ---------- | 
-| [Overture reference map](/#overture-data) | consists of the canonical datasets Overture releases each month |
-| [Global registry of GERS IDs](registry) | catalogs all GERS IDs ever published |
-| [Data changelog](changelog) | describes changes to entities across releases |
-| [Bridge files](bridge-files) | connects the GERS IDs in a release to the IDs from the underlying source data |
+| [Overture reference map](/getting-data/cloud-sources/) | consists of the canonical datasets Overture releases each month |
+| [Global registry of GERS IDs](./registry) | catalogs all GERS IDs ever published |
+| [Data changelog](./changelog) | describes changes to entities across releases |
+| [Bridge files](./bridge-files) | connects the GERS IDs in a release to the IDs from the underlying source data |
 
 
 ## How does GERS work?

docs/getting-data/cloud-sources.mdx

@@ -0,0 +1,108 @@
+---
+title: Accessing the Overture Catalog
+description: All of Overture's data offerings
+pagination_label: Full Data Catalog
+---
+
+
+Overture publishes global map data, vector tiles, a STAC catalog, a GERS registry, bridge files, and a changelog. Everything is hosted on Amazon S3 and Microsoft Azure Blob Storage with no account or credentials required. Databases, other cloud providers, overture tools, community tools, plugins for ArcGIS and QGIS
+
+## Published datasets
+
+Overture distributes its core datasets as [GeoParquet](https://geoparquet.org/) files, a column-oriented spatial data format optimized for cloud-native queries. You can scan across the files and pull only the data you need without downloading everything. To learn more about GeoParquet, consult [this guide](https://guide.cloudnativegeo.org/geoparquet/) from the Cloud Native Geospatial Forum.
+
+| Provider | Path |
+|---|---|
+| Amazon S3 | `s3://overturemaps-us-west-2/release/<RELEASE>` |
+| Microsoft Azure | `https://overturemapswestus2.blob.core.windows.net/release/<RELEASE>` |
+
+You can find the latest `<RELEASE>` value in the [STAC catalog](https://stac.overturemaps.org/catalog.json) or the [STAC viewer](https://stac.overturemaps.org/). See the [quick start](/getting-data/) for how to query the catalog programmatically.
+
+### Path structure
+
+The base paths above point to all of the data in a particular release — about 500 GB. Overture partitions the data by `theme` and `type`; specifying those directories in your path will significantly narrow the data you access.
+
+Here's an example S3 path to the `infrastructure` feature type in the `base` theme:
+
+```
+s3://overturemaps-us-west-2/release/2026-02-18.0/theme=base/type=infrastructure/*.parquet
+```
+
+The components in that path:
+
+- **`<RELEASE>`**: Overture releases data monthly following a date-based versioning scheme in the format `yyyy-mm-dd.x`. An archive of releases is maintained on both Amazon S3 and Azure Blob Storage.
+- **`<theme>`**: one of Overture's six data themes — addresses, base, buildings, divisions, places, and transportation — and the first level of partitioning of the data.
+- **`<type>`**: a feature type within a theme, e.g. `infrastructure` within `base`, and the second level of partitioning of the data.
+- **`*.parquet`**: the file type Overture uses to store and deliver the data. The `*` indicates you want all of the Parquet files in a particular directory.
+
+### Theme and type mapping
+
+| Theme | Feature types |
+|---|---|
+| [Addresses](/guides/addresses/) | address |
+| [Base](/guides/base/) | bathymetry, infrastructure, land, land_cover, land_use, water |
+| [Buildings](/guides/buildings/) | building, building_part |
+| [Divisions](/guides/divisions/) | division, division_area, division_boundary |
+| [Places](/guides/places/) | place |
+| [Transportation](/guides/transportation/) | segment, connector |
+
+### Downloading with the AWS CLI
+
+You can install the [AWS CLI](https://docs.aws.amazon.com/cli/latest/userguide/getting-started-install.html) to download Parquet files from S3 at the command line. In this example, you download all nine Parquet files (about one GB) in the `infrastructure` feature type:
+
+```bash
+aws s3 cp --no-sign-request --recursive \
+  s3://overturemaps-us-west-2/release/2026-02-18.0/theme=base/type=infrastructure/ .
+```
+
+### Downloading with AzCopy
+
+You can download files from Azure Blob Storage using [AzCopy](https://learn.microsoft.com/en-us/azure/storage/common/storage-use-azcopy-v10). Here's how to grab all of the `place` data:
+
+```bash
+azcopy copy \
+  "https://overturemapswestus2.dfs.core.windows.net/release/2026-02-18.0/theme=places/type=place/" \
+  "<local directory path>" --recursive
+```
+
+## PMTiles
+
+Overture generates [PMTiles](https://docs.protomaps.com/pmtiles/) vector tile archives with each release. Each theme is stored as a separate archive containing geometry and tags in MVT format. These tiles power the [Explore site](https://explore.overturemaps.org) and are designed for data inspection rather than production cartography.
+
+| Provider | Path |
+|---|---|
+| Amazon S3 | `s3://overturemaps-tiles-us-west-2-beta/<RELEASE>/<THEME>.pmtiles` |
+| HTTP | `https://overturemaps-tiles-us-west-2-beta.s3.amazonaws.com/<RELEASE>/<THEME>.pmtiles` |
+
+:::note
+The `<RELEASE>` in the tiles path uses the date only without the minor version, e.g. `2026-02-18` instead of `2026-02-18.0`.
+:::
+
+You can preview any PMTiles URL at [pmtiles.io](https://pmtiles.io). To create your own tiles from Overture data, see the [PMTiles example](/examples/overture-tiles/) and the [overture-tiles](https://github.com/OvertureMaps/overture-tiles) repository.
+
+## STAC catalog
+
+Overture publishes a [STAC catalog](https://stac.overturemaps.org/catalog.json) that serves as the machine-readable index for all releases. It always points to the latest release and includes metadata for every theme and feature type: spatial extents, feature counts, column names, links to GeoParquet files on AWS and Azure, and links to PMTiles. Browse it in the [STAC viewer](https://stac.overturemaps.org/).
+
+See the [quick start](/getting-data/) for examples of querying the STAC catalog in Python and DuckDB.
+
+## GERS registry, bridge files, and changelog
+
+Each release includes artifacts from the [Global Entity Reference System (GERS)](/gers/):
+
+- **[GERS Registry](/gers/registry/)**: a lookup service for GERS IDs that maps stable identifiers to their current feature data across releases.
+- **[Bridge files](/gers/bridge-files/)**: files that track how GERS IDs change between releases — additions, removals, and geometry updates.
+- **[Changelog](/gers/changelog/)**: a record of schema and data changes across releases.
+
+## Data mirrors
+
+In addition to the official sources, several partners maintain mirrors of the data on these platforms:
+
+- [BigQuery](/getting-data/data-mirrors/bigquery/)
+- [Databricks](/getting-data/data-mirrors/databricks/)
+- [Fused](/getting-data/data-mirrors/fused/)
+- [Source Cooperative](https://source.coop/)
+- [Snowflake](/getting-data/data-mirrors/snowflake/)
+- [Wherobots](/getting-data/data-mirrors/wherobots/)
+
+These are community-maintained resources that may offer different access patterns, additional tooling, or other platform-specific benefits.

docs/getting-data/data-mirrors/fused.mdx

@@ -1,5 +1,6 @@
 ---
 title: Fused 
+pagination_label: Fused
 ---
 
 import Tabs from '@theme/Tabs';

docs/getting-data/data-mirrors/snowflake.mdx

@@ -1,5 +1,6 @@
 ---
 title: Snowflake
+pagination_label: Snowflake
 ---
 
 Overture data is accessible through the [Snowflake Data Marketplace](https://app.snowflake.com/marketplace/providers/GZT0ZKUCHE3/CARTO) via public listings published and maintained by [CARTO](https://www.carto.com). Below is a step-by-step guide on how to access and use the data:

docs/getting-data/duckdb.mdx

@@ -1,5 +1,7 @@
 ---
 title: DuckDB
+description: Querying the Overture datalog
+pagination_label: DuckDB
 ---
 
 import Tabs from '@theme/Tabs';

docs/getting-data/explore.mdx

@@ -0,0 +1,47 @@
+---
+title: Explorer
+description: No-code tool for exploring and downloading data
+pagination_label: Explorer
+---
+
+---
+
+import { ExplorerEmbed } from '@site/src/components/home';
+
+
+[Overture Maps Explorer](https://explore.overturemaps.org) is a browser-based tool for browsing and inspecting Overture data. No installation, no account, no SQL — just open the link and start exploring.
+
+<ExplorerEmbed src="https://explore.overturemaps.org/#map=12/39.9526/-75.1652" />
+
+## Explore and Inspect modes
+
+The Explorer has two distinct modes.
+
+**Explore mode** presents the data as a styled, readable map. You can browse places, roads, buildings, land use, and water features with cartographic styling that makes the data easy to understand at a glance. A layers panel lets you toggle feature types on and off, and you can click individual features to see their properties.
+
+**Inspect mode** is closer to the raw data. The layers panel reorganizes to mirror the Overture schema itself: base breaks down into land, water, bathymetry, land cover, and land use; transportation shows segments and connectors; divisions show areas, boundaries, and labels. Every feature type is symbolized uniformly as simple points, lines, and polygons. This is the mode for developers and data users who want to see exactly what's in the tiles.
+
+## Multilingual names
+
+The Explorer surfaces the richness of Overture's `names` property. Toggle between languages to see place names rendered in dozens of scripts and languages. This comes directly from the names field in Overture data, which carries common names, alternate names, and translations contributed by sources around the world.
+
+## How it works
+
+The Explorer is built with [MapLibre GL JS](https://maplibre.org/) and powered by [PMTiles](https://docs.protomaps.com/pmtiles/) vector tile archives generated from each Overture release. The tiles are hosted on S3 and loaded directly in the browser — no tile server required.
+
+The site's styling is built on a token-based design system: primitives (color palette, fonts), semantics (feature-level color assignments), and components (stylesheet properties referencing the semantic values). This architecture makes it straightforward to create new map styles — a dark mode, for example, requires changing just one file.
+
+The Explorer is pinned to a specific Overture release, and updates go through a validation process that audits stylesheets against the current schema and tile metadata before they reach the live site.
+
+The source code is available in the [explore-site](https://github.com/OvertureMaps/explore-site) repository.
+
+## When to use it
+
+The Explorer is the fastest way to answer questions like:
+
+- What does Overture data look like in a specific area?
+- What properties are available on a building or a road segment?
+- How does Overture represent a particular place or address?
+- What's the GERS ID for a specific feature?
+
+It's a good starting point before writing queries with the [Python CLI](/getting-data/overturemaps-py/) or [DuckDB](/getting-data/duckdb/).