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4 changes: 2 additions & 2 deletions docs/guides/addresses.mdx
Original file line number Diff line number Diff line change
Expand Up @@ -29,9 +29,9 @@ Address data can be used for a variety of purposes, which can include:
- **Normalization**: Adhering to standard and consistent forms of address components.
- **Validation and Verification**: Confirming an address exists within a known list of addresses.

| ![Overture address coverage](/img/addresses/address-coverage-h3-aug.png) |
| ![Overture address coverage](/img/addresses/address-coverage-h3-sept.png) |
|:--:|
| *August coverage of Overture address data, visualized using H3 cells.* |
| *September coverage of Overture address data, visualized using H3 cells.* |

## Dataset description

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10 changes: 5 additions & 5 deletions docs/guides/buildings.mdx
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Expand Up @@ -74,12 +74,12 @@ Currently, the Overture buildings dataset is a combination of the following open

| source | type | conflation priority | count |
| --- | --- | --- | --- |
| [OpenStreetMap](//osm.org) | Community-contributed | 1 | ~635 Million |
| [Esri Community Maps](https://communitymaps.arcgis.com/home) | Community-contributed | 2 | ~11 Million |
| [Instituto Geográfico Nacional (España)](https://www.ign.es/) | National dataset | 3 | ~11.7 Million |
| [City of Vancouver, Canada](https://opendata.vancouver.ca/) | Municipal dataset | 4 | ~14 Thousand |
| [OpenStreetMap](//osm.org) | Community-contributed | 1 | ~656 Million |
| [Esri Community Maps](https://communitymaps.arcgis.com/home) | Community-contributed | 2 | ~17.5 Million |
| [Instituto Geográfico Nacional (España)](https://www.ign.es/) | National dataset | 3 | ~12.9 Million |
| [City of Vancouver, Canada](https://opendata.vancouver.ca/) | Municipal dataset | 4 | ~17 Thousand |
| [Google Open Buildings](https://sites.research.google/open-buildings/) | ML-derived roofprints (>90% precision) | 5 | ~350 Million |
| [Microsoft](https://github.com/microsoft/GlobalMLBuildingFootprints) | ML-derived roofprints | 6 | ~723 Million |
| [Microsoft](https://github.com/microsoft/GlobalMLBuildingFootprints) | ML-derived roofprints | 6 | ~805 Million |
| [Google Open Buildings](https://sites.research.google/open-buildings/) | ML-derived roofprints (\<90% precision) | 7 | ~650 Million |
| [Buildings in East Asian Countries](https://zenodo.org/records/8174931) | ML-derived roofprints | 8 | ~213 Million |

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48 changes: 34 additions & 14 deletions docs/guides/places.mdx
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Expand Up @@ -24,23 +24,43 @@ This guide is focused on the Overture places **data**—its content, scope, prop

The Overture places theme has one feature type, called `place`, and contains more than 64 million point representations of real-world entities: schools, businesses, hospitals, religious organizations, landmarks, mountain peaks, and much more. The theme is derived from a conflation of Meta, Microsoft, and PinMeTo data and is available under a [CDLA Permissive 2.0](https://cdla.dev/permissive-2-0/) license.

| ![Overture places theme coverage](/img/places/places-coverage-h3-aug.png) |
| ![Overture places theme coverage](/img/places/places-coverage-h3-sept.png) |
|:--:|
| *Overture places data, August 2025* |
| *Overture places data, September 2025* |

| source | feature count, August 2025 |
| sources (ordered) | feature count, September 2025 |
| --- | --- |
| meta | 56,659,810 |
| Microsoft | 5,369,523 |
| meta & Microsoft | 3,169,090 |
| Microsoft & PinMeTo | 74,744 |
| PinMeTo | 19,132 |
| meta, Microsoft & PinMeTo | 33,899 |
| Krick & Microsoft | 6,355 |
| meta & PinMeTo | 3,707 |
| Krick | 3,140 |
| Krick, meta & Microsoft | 2,717 |
| Krick & meta | 706 |
| meta | 56,229,637 |
| Foursquare | 6,089,297 |
| Microsoft | 5,322,704 |
| meta & Microsoft | 2,613,673 |
| meta & SparkGeo-confidence-conflation | 541,168 |
| meta, Microsoft & SparkGeo-confidence-conflation | 404,772 |
| Microsoft & meta | 207,468 |
| PinMeTo & Microsoft | 71,793 |
| Microsoft & SparkGeo-confidence-conflation | 62,751 |
| PinMeTo, meta & Microsoft | 31,490 |
| PinMeTo | 16,184 |
| Microsoft, meta & SparkGeo-confidence-conflation | 7,175 |
| Microsoft & Krick | 6,301 |
| Microsoft & PinMeTo | 6,004 |
| Krick | 3,172 |
| PinMeTo & meta | 2,780 |
| meta, Microsoft & Krick | 2,643 |
| PinMeTo, Microsoft & meta | 2,285 |
| Foursquare & SparkGeo-confidence-conflation | 1,067 |
| meta & Krick | 679 |
| meta, Microsoft & PinMeTo | 647 |
| Microsoft, meta & PinMeTo | 362 |
| meta & PinMeTo | 307 |
| Microsoft, meta & Krick | 64 |
| Microsoft, Krick & meta | 43 |
| PinMeTo, meta, Microsoft & SparkGeo-confidence-conflation | 4 |
| Krick & meta | 16 |
| PinMeTo, Microsoft & SparkGeo-confidence-conflation | 2 |
| Microsoft, PinMeTo & SparkGeo-confidence-conflation | 1 |
| PinMeTo, meta & SparkGeo-confidence-conflation | 1 |
| meta, Microsoft, PinMeTo & SparkGeo-confidence-conflation | 1 |

## Dataset description

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76 changes: 38 additions & 38 deletions docs/guides/transportation.mdx
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Expand Up @@ -98,45 +98,45 @@ Transportation segments are divided into three subtypes: **rail**, **water**, an
<Tabs>
<TabItem value="classes" label="classes" default>

| subtype | class | subclass | feature count, August 2025 |
| subtype | class | subclass | feature count, September 2025 |
| --- | --- | --- | --- |
| rail | funicular | | 1,309 |
| rail | light_rail | | 16,218 |
| rail | monorail | | 3,023 |
| rail | narrow_gauge | | 31,044 |
| rail | standard_gauge | | 1,429,962 |
| rail | subway | | 52,417 |
| rail | tram | | 58,992 |
| rail | unknown | | 376,475 |
| road | bridleway | | 99,768 |
| road | cycleway | cycle_crossing | 57,084 |
| road | cycleway | | 1,272,514 |
| road | footway | crosswalk | 2,189,913 |
| road | footway | sidewalk | 3,532,386 |
| road | footway | | 15,891,996 |
| road | living_street | | 3,322,367 |
| road | motorway | link | 640,036 |
| road | motorway | | 433,368 |
| road | path | | 13,455,216 |
| road | pedestrian | | 464,602 |
| road | primary | link | 490,259 |
| road | primary | | 6,681,557 |
| road | residential | | 125,698,481 |
| road | secondary | link | 388,309 |
| road | secondary | | 10,716,541 |
| road | service | alley | 1,700,121 |
| road | service | driveway | 16,639,987 |
| road | service | parking_aisle | 6,322,008 |
| road | service | | 32,980,815 |
| road | steps | | 1,863,964 |
| road | tertiary | link | 297,428 |
| road | tertiary | | 19,828,207 |
| road | track | | 25,187,115 |
| road | trunk | link | 552,618 |
| road | trunk | | 3,443,606 |
| road | unclassified | | 29,598,013 |
| road | unknown | | 1,279,752 |
| water | | | 28,259 |
| rail | funicular | | 1,309 |
| rail | light_rail | | 16,302 |
| rail | monorail | | 3,037 |
| rail | narrow_gauge | | 31,105 |
| rail | standard_gauge | | 1,432,742 |
| rail | subway | | 52,834 |
| rail | tram | | 59,075 |
| rail | unknown | | 378,647 |
| road | bridleway | | 100,550 |
| road | cycleway | cycle_crossing | 57,957 |
| road | cycleway | | 1,279,784 |
| road | footway | crosswalk | 2,219,681 |
| road | footway | sidewalk | 3,595,350 |
| road | footway | | 16,025,515 |
| road | living_street | | 3,332,042 |
| road | motorway | link | 640,561 |
| road | motorway | | 433,073 |
| road | path | | 13,523,391 |
| road | pedestrian | | 465,722 |
| road | primary | link | 491,060 |
| road | primary | | 6,698,107 |
| road | residential | | 125,885,875 |
| road | secondary | link | 389,359 |
| road | secondary | | 10,737,084 |
| road | service | alley | 1,716,773 |
| road | service | driveway | 16,761,777 |
| road | service | parking_aisle | 6,355,124 |
| road | service | | 33,109,690 |
| road | steps | | 1,878,101 |
| road | tertiary | link | 297,561 |
| road | tertiary | | 19,881,087 |
| road | track | | 25,289,639 |
| road | trunk | link | 554,871 |
| road | trunk | | 3,456,718 |
| road | unclassified | | 29,648,979 |
| road | unknown | | 1,710,480 |
| water | | | 28,327 |

</TabItem>
</Tabs>
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