Agroforestry Tree Species Identification (India, Multimodal)

A multimodal tree species identification dataset covering 23,156 individual trees in agroforestry settings across Karnataka and Rajasthan, India. Each example links tree-level metadata and species labels with SkySat RGB crops, Sentinel-1 time series, Sentinel-2 time series, PlanetScope time series, and derived species-classification vectors. Raw Google Street View imagery is not redistributed; panorama IDs are retained to support reproducible workflows for users with appropriate API access.

remote-sensing agriculture agroforestry tree-species multimodal

Ajmer
Alwar
Ballari
Balotra
Barmer
Bengaluru South
Bharatpur
Bhilwara
Bikaner
Bundi
Chikkaballapura
Chitradurga
Chittorgarh
Churu
Dakshina Kannada
Dausa
Davanagere
Deeg
Dharwad
Gadag
Hanumangarh
Hassan
Jaipur
Jaisalmer
Jalore
Jhunjhunu
Jodhpur
Karauli
Kolar
Koppal
Kota
Kotputli-Behror
Mandya
Nagaur
Pali
Phalodi
Pratapgarh
Raichur
Sawai Madhopur
Shimoga
Sikar
Tonk
Tumakuru
Udaipur
Udupi
Uttara Kannada
Vijayanagara

center east north northeast northwest south southeast southwest west

lat

[latlon] Latitude of the sample/location point

lon

[latlon] Longitude of the sample/location point

source

[placename] City or district name for the sample location (e.g. Ajmer, Ballari)

[latlon] Latitude of the Google Street View capture point

[latlon] Longitude of the Google Street View capture point

state

[placename] Indian state name: Karnataka or Rajasthan

[latlon] Original detection latitude before any snapping or adjustment

[latlon] Original detection longitude before any snapping or adjustment

[geometry] Bounding box of the tree crown

3 files sampled for this table

manifest

[latlon] Latitude of the individual tree in the release manifest

[latlon] Longitude of the individual tree in the release manifest

[geometry] Western edge of tree bounding box (combined with bbox_south/east/north)

region

[placename] Named region or district associated with the tree

3 files sampled for this table

[latlon] Original detection latitude

[latlon] Original detection longitude

2 files sampled for this table

[latlon] Original detection latitude

[latlon] Original detection longitude

2 files sampled for this table

Source Cooperative product page

## README.md
# Species Identification Dataset

This release contains a no-GSV multimodal tree species identification dataset organized around individual trees.

Raw Google Street View images are not redistributed. Panorama IDs and related metadata are retained, and code is released separately to repopulate GSV imagery from those panorama IDs where users have appropriate API access and rights.

## Contents

- `tree_metadata.csv`: sanitized tree-level metadata.
- `gsv_pano_ids.csv`: tree-level mapping to GSV panorama IDs and related capture/geometry fields.
- `all_metadata.json`: sanitized nested metadata.
- `instances_default.json`: annotation export.
- `all_locations.csv`, `dataset_summary.json`, `dataset_and_labels_context.txt`: dataset context files.
- `skysat_crops/`: one canonical RGB JPEG per example.
- `sentinel1_pixel_timeseries/pixel_data/`: Sentinel-1 tree time series, one CSV per example.
- `sentinel2_pixel_timeseries/pixel_data/`: Sentinel-2 tree time series, one CSV per example.
- `planetscope_pixel_timeseries/pixel_data/`: PlanetScope tree time series, one CSV per example.
- `species_classification_vectors_5full/`: full-modality species vector outputs.
- `manifest/`: release manifests and audit files.
- `sample_100/`: small reviewer-inspection sample with 100 complete examples across modalities.

## Counts

- Examples: 23,156 individual trees.
- Raw Google Street View images included: no.
- Modalities included: SkySat RGB crops, Sentinel-1 time series, Sentinel-2 time series, PlanetScope time series, and derived species-classification vectors.

## Reproducibility

The release can be used without raw GSV images using the released remote-sensing modalities, labels, metadata, and vectors. Users who have appropriate API access and rights can use the retained panorama IDs with separately released code to repopulate GSV images through the provider-approved access path.

## License

This dataset release is licensed under [Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)](https://creativecommons.org/licenses/by-nc/4.0/).

Upstream and optional sources remain subject to their own terms: for example, Google Street View / Maps Platform rules if you fetch imagery from panorama IDs, Copernicus Sentinel and Planet policies for underlying satellite products, and any provider-specific restrictions noted in `croissant.json` provenance. The CC BY-NC license applies to this curated release as a whole; it does not override third-party licenses for data you obtain separately from those providers.

providers: role [producer] for 'Planet Labs' is an AI guess — Provider of SkySat and PlanetScope satellite imagery used as upstream source modalities; README lists them as an upstream source subject to their own terms, not as an explicit dataset producer.. Verify before flipping to _state: reviewed.

providers: role [producer] for 'ESA / Copernicus' is an AI guess — Provider of Sentinel-1 and Sentinel-2 imagery used as upstream source modalities; README lists Copernicus Sentinel as an upstream source, not an explicit dataset producer.. Verify before flipping to _state: reviewed.

providers: role [producer] for 'Google' is an AI guess — Provider of Street View panoramas (panorama IDs retained but imagery not redistributed); README lists Google Street View / Maps Platform as an upstream source subject to its own terms.. Verify before flipping to _state: reviewed.

extent.spatial.bbox: omitted; spatial reference is categorical via [..lat (latlon), ..lon (latlon), ..tree_lat (latlon), ..tree_lon (latlon), ..gsv_lat (latlon), ..gsv_lon (latlon), ..original_lat (latlon), ..original_lon (latlon), ..source (placename), ..state (placename), ..tree_bbox (geometry), manifest.tree_lat (latlon), manifest.tree_lon (latlon), manifest.bbox_west (geometry), manifest.region (placename), species_classification_vectors_5full/data_splits.tree_lat (latlon), species_classification_vectors_5full/data_splits.tree_lon (latlon), species_classification_vectors_5full/data_splits.original_lat (latlon), species_classification_vectors_5full/data_splits.original_lon (latlon), species_classification_vectors_5full/metadata.tree_lat (latlon), species_classification_vectors_5full/metadata.tree_lon (latlon), species_classification_vectors_5full/metadata.original_lat (latlon), species_classification_vectors_5full/metadata.original_lon (latlon)]. Add bbox once the placename → bbox gazetteer covers it.


ESRI Tutorial Data

Data used for exercises in collaboration with ESRI showing the fundamentals of optical satellite imagery resolution: Spatial, Spectral, Temporal and Radiometric.


Field boundaries created by Planet's Automated Field Boundary detection algorithm

A pan-European vector dataset of agricultural field boundaries automatically delineated from Copernicus Sentinel-2 satellite imagery acquired in May–June 2022, covering approximately 54 country-level GeoParquet files with over 154 million field polygons. Each polygon represents a spatially homogeneous agricultural parcel and includes quality metrics from Planet's automated field boundary detection algorithm.

field-boundaries agriculture remote-sensing arable-land land-use land-parcels deep-learning machine-learning sentinel-2

Field Boundaries

Source Cooperative product page

Proprietary License

## README.md
# EU field boundaries
_A vector dataset of Field Boundaries, automatically delineated from Sentinel-2 satellite imagery from May-June 2022._

Automatic field delineation refers to the process of automatically tracing the boundaries of agricultural parcels from satellite or aerial imagery. We consider an agricultural parcel as a spatially homogeneous land unit used for agricultural purposes, where a single crop is grown. The result of the FD is a set of closed vector polygons marking the extent of each agricultural parcel. Such polygons are the input to a multitude of applications, ranging from the management of agricultural resources, such as the Area Monitoring for the Common Agricultural Policy, to precision farming, to the estimation of damages to crop yield due to natural (e.g. drought, floods), and human-made disasters (e.g. war). Automatic estimation of parcels with high fidelity in a timely manner allows therefore to characterize the changes of agricultural landscapes due to anthropogenic activities, agricultural practices, and climate change consequences.

<img src="https://data.source.coop/planet/eu-field-boundaries/eu-fb.png" alt="AOI of the dataset" width="100%"/>

The dataset was created using Copernicus Sentinel-2 data for May 2022, provided by [@sinergise](https://sinergise.com).

## Dataset License

[CreativeCommons BY-SA](https://creativecommons.org/licenses/by-sa/4.0/)

## Links
  * [Fiboa](https://github.com/fiboa/data-survey/blob/main/data/Planet.md) dataset description
  * Automatic Field Delineation [Blog post](https://medium.com/sentinel-hub/automatic-field-delineation-new-release-1c2938399f0)

## Authors

  [Sinergise](https://www.sinergise.com)
  * Matej Batic
  * Devis Peressutti
  * Nejc Vesel
  * Sara Verbic
  * Matic Lubej
  * Nika Oman Kadunc

  [Planet](https://planet.com)
  * Chris Holmes

## Acknowledgements

This work has received funding from the European Union's Horizon Europe research and innovation programme under grant agreement No. 101059548.

For more info about the project, see [OpenEarthMonitor](https://earthmonitor.org).

providers: role [licensor] for 'European Union / Horizon Europe' is an AI guess — Funder via grant No. 101059548 (OpenEarthMonitor); assumed licensor role is uncertain — they funded the work but may not hold IP. Verify before flipping to _state: reviewed.


99 million agricultural parcel boundaries with harmonized crop type information across seven European countries (Austria, Denmark, France, Germany, Netherlands, Portugal, Spain) covering 2018-2024. Derived from EU IACS programme cadastral and crop data with improved geometries and additional attributes for machine learning and Earth observation applications.

agriculture field-boundaries crop-classification land-parcels remote-sensing

Dataset files (75 x .parquet)

Source Cooperative product page

## README.md
# RapidCrops

## Data coverage

Dense temporal coverage (2018-2022) across seven countries:
- Austria
- Denmark
- Spain (partial coverage in Catalonia only)
- France
- Germany (partial coverage in Brandenberg, Nordrhein Westfalen & Lower Saxony)
- Netherlands
- Portugal

Additional years of coverage are included for:
- Germany (2023 & 2024)
- France (2023)
- Netherlands (2023 & 2024)

Datasets are published in two forms:
1. "National": National datasets are provided individually for each year of coverage.
2. "Combined": All national datasets are combined into a single partitioned (by country) dataset for a given year.

## Data structure

Because all data resides within Europe, boundary geometries are all provided in `EPSG:3035`.

See below an example GeoParquet schema for the France 2020 dataset:

## Usability and efficiency

Redundant vertices were removed from the boundary polygons with a tolerance of 20cm to protect the integrity of the parcel.
This resulted in a >25% reduction in the number of vertices, allowing for more efficient spatial queries.

## Assessing suitability for combining parcels with EO products

Identifying parcels from which clean pixels can be obtained from satellite imagery depends on both the size of a parcel and the regularity of its shape.
RapidCrops provides attributes to help quantify these properties:
- `parcel_width`: The length of the shortest edge on the parcel boundary's minimum rotated rectangle
- `area_ratio_hull`: The ratio between the area of the parcel boundary, and that of its convex hull
- `max_contained_radius`: The radius of the parcel boundary's maximum inscribed circle
Knowing this can help to filter out parcels from which meaningful analysis cannot be performed, or to help explain variation in performance across parcels.

These metrics can be used to identify suitable parcels for analysis by a given EO sensor, as highlighted below:

## Facilitating spatial stratification

Grid cell IDs from two popular spatial gridding systems (Quadkey & H3) are included for each boundary to allow for spatially-stratified sampling. The gridding systems are hierarchical and coarser grid resolutions can be derived form the values provided according to the user’s need.

Below is the quadkey spatial gridding at the resolution provided:

But coarser resolution gridding can be derived by reading a subset of the leftmost characters in the key:

## Accessing the data

```python
import geopandas as gpd

url = "s3://planet/rapidcrops/national/AUT/rapidcrops_2018_aut.geo.parquet"

# Retrieve a full dataset
df = gpd.read_parquet(url)

# OR, filter the dataset on a bbox for faster retrieval
df = gpd.read_parquet(url, bbox=bbox)
```

## Underlying data licenses

The data is made available subject to the licenses associated with the underlying source datasets. No additional limitations or conditions are applied. See below for links to the underlying license information for each source dataset:

| Source dataset        | Dataset license       |
|-----------------------|-----------------------|
| Austria               | [INSPIRE public access license](http://inspire.ec.europa.eu/metadata-codelist/LimitationsOnPublicAccess/noLimitations) & [CC-BY-AT 4.0](https://creativecommons.org/licenses/by/4.0/)                     |
| Denmark               |   [INSPIRE public access license](http://inspire.ec.europa.eu/metadata-codelist/LimitationsOnPublicAccess/noLimitations) & [INSPIRE no conditions](http://inspire.ec.europa.eu/metadata-codelist/ConditionsApplyingToAccessAndUse/noConditionsApply) & [CC0 1.0 Universal](https://creativecommons.org/publicdomain/zero/1.0/deed.en)                   |
| France                |   [Custom open license](https://www.etalab.gouv.fr/wp-content/uploads/2014/05/Open_Licence.pdf)                    |
| Germany               |   Custom open licenses: [NRW](http://dcat-ap.de/def/licenses/dl-by-de/2.0), [Brandenberg](https://www.govdata.de/dl-de/by-2-0), [LS](http://dcat-ap.de/def/licenses/dl-by-de/2.0)                    |
| Netherlands           |   [INSPIRE public access license](http://inspire.ec.europa.eu/metadata-codelist/LimitationsOnPublicAccess/noLimitations) & [INSPIRE no conditions](http://inspire.ec.europa.eu/metadata-codelist/ConditionsApplyingToAccessAndUse/noConditionsApply) & [Dutch creative commons license](http://creativecommons.org/publicdomain/mark/1.0/deed.nl)                    |
| Portugal              |  [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)                     |
| Spain                 |  [Custom open license](https://administraciodigital.gencat.cat/ca/dades/dades-obertes/informacio-practica/llicencies/#limitacio-de-responsabilitat)                     |

## Suggested citation

```
Holden, P., Davis, T., Holmes, C., Senaras, C., & Wania, A. (2025). RapidCrops: A pan-European label dataset for large-scale crop classification (v1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.15166359
```

In BibTeX format:

```
@dataset{rapidcrops,
  title = {RapidCrops: A pan-European label dataset for large-scale crop classification},
  publisher = {Zenodo},
  doi = {https://doi.org/10.5281/zenodo.15166359},
  year = {2025},
  urldate = {<date of access, ISO>},
  author = {
    Holden, Piers and
    Davis, Timothy and
    Holmes, Christopher and
    Senaras, Caglar and
    Wania, Annett
  },
  version = {1.0},
}
```


SkyScraper

Skyscraper pairs multi-temporal Earth-observation imagery with GDELT-derived news text: each record links geolocated PlanetScope or Sentinel-2 image sequences to event-centric captions and article metadata.


OpenStreetMap Open Data Products

Cloud-native vector tile distributions of OpenStreetMap and Natural Earth geodata in PMTiles format, covering Web Mercator zoom levels 0–15. Data is organized into thematic layers (water, buildings, landuse, etc.) and mirrored daily from Protomaps builds. These are lossy transformations of OSM planet data intended for basemap rendering.

openstreetmap administrative-boundaries vector-tiles pmtiles land-use building-footprints natural-earth

Dataset files (4 x .pmtiles)

Source Cooperative product page

## README.md

# OpenStreetMap

Distributions of OpenStreetMap vector geodata in cloud-native formats.

These datasets are "lossy" transformations of open source vector data from [planet.openstreetmap.org](planet.openstreetmap.org) and [Natural Earth](http://naturalearthdata.com).
Includes a subset of OSM features at Web Mercator zoom levels 0-15, organized into themes like `water`, `buildings` and `landuse`. The included features are documented at [Protomaps Basemap Layers](https://docs.protomaps.com/basemaps/layers).

## Tiles

`tiles/v3.pmtiles`: Latest basemap version. Mirrored daily from [maps.protomaps.com/builds](https://maps.protomaps.com/builds).

`tiles/v2/`: Version 2 of basemaps.

* Files can be viewed directly in Source Viewers.
* source.coop URLs can be viewed directly in the [PMTiles Viewer](https://protomaps.github.io/PMTiles/).
* Extract a spatial subset from the source.coop URL using the [pmtiles cli](https://docs.protomaps.com/pmtiles/cli#extract).

## Limitations

Limitations

* We don't recommend cross-origin hotlinking directly to source.coop URLs.
* Data reflects OpenStreetMap as of that day.

## License

Data products are Produced Works of the [Open Database License (ODbL)](https://opendatacommons.org/licenses/odbl/1-0/).

providers: role [processor, host] for 'Protomaps' is an AI guess — Assumed processor/host based on daily mirror from maps.protomaps.com/builds; README does not explicitly state roles.. Verify before flipping to _state: reviewed.


Red_Riego_Madrid

Municipal irrigation network infrastructure dataset for the city of Madrid, Spain, containing spatial records of irrigation sectors and valve-controlled irrigation lots. Each record includes irrigation type, electrovalve type, pipe caliber, and inventory identifiers linked to Madrid's infrastructure management system (MINT). The dataset covers the Madrid urban area with geometries in OGC CRS84.

hydrology irrigation urban-infrastructure geoparquet madrid

lote3

geometry

[geometry] OGC CRS84 polygon/linestring geometry of irrigation lot infrastructure elements

STRUCT(xmin FLOAT, ymin FLOAT, xmax FLOAT, ymax FLOAT)

2 files sampled for this table

sectores_riego_lote2_lote3_.parquet

[geometry] OGC CRS84 geometry of irrigation sectors

3 files sampled for this table

Source Cooperative product page

license: AI returned 'unknown' (README does not state a license). Verify with publisher before flipping to _state: reviewed.

providers: role [producer, licensor] for 'Ayuntamiento de Madrid' is an AI guess — Inferred as municipal data producer based on dataset name, bounding box covering Madrid, and MINT (Madrid infrastructure inventory) identifiers; no README confirms the publisher.. Verify before flipping to _state: reviewed.

extent.spatial.bbox: omitted; spatial reference is categorical via [lote3.geometry (geometry), sectores_riego_lote2_lote3_.geometry (geometry)]. Add bbox once the placename → bbox gazetteer covers it.


GeoMIP_zarr

Climate model simulation output from the Geoengineering Model Intercomparison Project (GeoMIP), spanning multiple Earth system models. Simulations explore proposed geoengineering interventions and their climate effects, reformatted into cloud-native Zarr for efficient remote access.

geoengineering climate-modeling climate earth-system-models model-intercomparison solar-radiation-management climate-simulation

Dataset files (1 x .zarr)

Source Cooperative product page

providers: role [producer, licensor] for 'GeoMIP (Geoengineering Model Intercomparison Project)' is an AI guess — GeoMIP coordinates the simulation protocol; individual modeling centers are the actual producers — no README to confirm.. Verify before flipping to _state: reviewed.

description: AI says 'multiple Earth system models' / multiple simulations, but the hosted tree is a single model (MIROC-ES2H), single experiment (G6-1p5k-SAI). No README exists in the repo (0 of 1905 files) — AI extrapolated from the 'GeoMIP' title. Narrow to the actual single-model/single-experiment scope before flipping to reviewed.

temporal: extent is [null, null] — probe_zarr reads only the root group (a hierarchy container), so the nested time arrays (.../time/zarr.json) are unread, and there is no README to supply dataset temporal. Recoverable only via the open_datatree recursion gap (affects all zarr products).


Public Trees in the Netherlands - Test Bucket

Bucket used for testing of public trees in NL dataset

geoparquet/ : Not useful

nl_trees/ : Not useful

nl_trees_2/: Dutch tree dataset

nl_trees_no_hilbert/: Dutch tree dataset without spatial sorting, for testing purposes


Flight Counts


Flight Tracks

AeroWhere Flight Tracks is a database of historic aircraft flight track segments over the contiguous United States (CONUS), derived from ADS-B (Automatic Dependent Surveillance–Broadcast) position reports captured by ground receiver networks. Each record is a GIS linestring geometry for a single flight track segment, attributed with ICAO hex ID, aircraft registration, type, UTC timestamps, and height above ground level (AGL) computed using a USGS 3DEP digital elevation model. Data are stored as hive-partitioned GeoParquet files indexed by zoom-level-8 quadkey, year, month, and day.

geoparquet time-series digital-elevation-model aviation flight-tracking ads-b trajectories

flights-2023-09-26.parquet

[geometry] 2D GeoArrow linestring representing a single flight track segment in EPSG:4326

[spatial_index] Zoom-level-8 quadkey (EPSG:3857 WebMercatorQuad) used for hive partitioning

flights-2024-10-04.parquet

flights-2024-11-11.parquet

flights-2025-06-05.parquet

flights-2024-12-23.parquet

flights-2024-08-10.parquet

flights-2023-11-20.parquet

flights-2024-01-22.parquet

flights-2025-08-08.parquet

flights-2024-01-13.parquet

flights-2024-12-09.parquet

flights-2023-12-27.parquet

flights-2024-01-17.parquet

flights-2023-07-16.parquet

flights-2023-07-03.parquet

flights-2023-04-12.parquet

flights-2025-03-29.parquet

flights-2023-10-25.parquet

flights-2025-10-07.parquet

flights-2025-04-19.parquet

flights-2023-08-24.parquet

flights-2024-10-01.parquet

flights-2024-10-30.parquet

flights-2025-04-28.parquet

flights-2024-07-31.parquet

flights-2024-08-15.parquet

flights-2024-09-18.parquet

flights-2025-03-21.parquet

flights-2025-09-14.parquet

flights-2026-01-02.parquet

flights-2023-04-06.parquet

Source Cooperative product page

## README.md
# AeroWhere Flight Tracks
## Description
The AeroWhere Flight Tracks database captures historic aircraft flight tracks as GIS linestring geometries, attributed with height above ground level (AGL) and other identifying information about the flight (timestamp, ICAO, aircraft type, etc.). The currently available data is limited to the contiguous United States (CONUS) and includes flights spanning the time period noted in the accompanying [Date Range](https://data.source.coop/root-geospatial/flight-tracks/DATE-RANGE.md) file.

The flight tracks are derived from Automatic Dependent Surveillance–Broadcast (ADS-B) position traces, captured by a network of ground receivers, collected and made available via [ADS-B LOL historical data archive](https://www.adsb.lol/docs/open-data/historical/) (see [Source Data](#source-data) for more information).

The source ADS-B files have been parsed into GIS format by connecting ADS-B positions of each unique aircraft in chronological order. Gaps between consecutive locations ≥ 30 km were not connected to limit uncertainty about aircraft positioning in spans of this size. Altitudes were converted to height above ground through the use of a digital elevation model (DEM) (see [Source Data](#source-data)). Only a select set of attributes from the source ADS-B files are currently included in the flight tracks dataset (see [Schema](#schema)).

[![AeroWhere Flight Tracks Screenshot](https://data.source.coop/root-geospatial/flight-tracks/docs/banner.png "AeroWhere Flight Tracks Viewer")](https://aerowhere.com/flight-tracks?lat=40.7178&lng=-74.0952&zoom=10.00&date=2023-11-27)

## Data Format
### GeoParquet
The data are provided as a hive-partitioned GeoParquet dataset. The GeoParquet files follow the specification for GeoParquet v1.1.0 and geometries are encoded as 2-D linestrings in GeoArrow format.

Each geometry represents a single segment of a flight track. Geometries are encoded in the direction of travel of the flight track (i.e., first vertex is the start of the segment, last vertex is the end of the segment). All geometries are in the EPSG:4326 coordinate reference system.

### Partitioning
The dataset uses hive partitioning to split the dataset into files of reasonable size to enable faster acess. The partitioning scheme is intended to be spatiotemporal, first splitting by year, month, and day. The structure is:

`quadkey_z8={quadkey}/year={year}/month={month}/day={day}/flights-{year}-{MONTH}-{DAY}.parquet`

where:
- `quadkey` is the zoom level 8 tile quadkey in the spherical mercator EPSG:3857 grid
- `year` is the 4-digit year of the partition (e.g., `2025`)
- `month` is the month of the partition *without* zero padding (i.e., `1`, `2`, ..., `12`)
- `MONTH` is the month of the partition *with* zero padding (i.e., `01`, `02`, ..., `12`)
- `day` is the day of the partition  *without* zero padding (i.e., `1`, `2`, ..., `31`)
- `DAY` is the day of the partition  *with* zero padding (i.e., `01`, `02`, ..., `31`)

> **Important** \
> The tile based partitioning is based on spatial intersection between flight tracks and the tiles. As a result, a single flight track may occur across multiple partitions if it overlaps multiple tiles. For certain uses cases, this means you may need to deduplicate records. This can be done using the `end_t` (or `start_t`) and `icao` fields as a compound identifier for unique flight segments (see [Data Access Examples](#data-access-examples-python)). Note that this also means that the data for a single partition will not strictly adhere to the tile bounds (i.e., the data won't be clipped exactly to the tile edges).

### Schema
Each partition uses the following schema:
- `geometry` (list[struct(x, y)]): 2D linestring geometry representing a single segment of a flight track.
- `icao` (string): Hex ID of the aircraft
- `r` (string): Aircraft registration
- `aircraft_type` (string): Aircraft type
- `desc` (string): Aircraft type description
- `start_t` (timestamp[ns]): Timestamp (in UTC) for the start (first vertex) of the segment.
- `end_t` (timestamp[ns, tz=UTC]): Timestamp (in UTC) for the end (last vertex) of the segment.
- `start_height_agl_ft` (int32): Height above ground level in feet for the start (first vertex) of the segment. Derived from altitude reported in ADS-B and ground elevation from DEM.
- `end_height_agl_ft` (int32): Height above ground level in feet for the end (last vertex) of the segment. Derived from altitude reported in ADS-B and ground elevation from DEM.

## Data Access Examples (Python)
Required dependencies:
- geopandas: https://geopandas.org/en/stable/
- pyarrow: https://arrow.apache.org/docs/python/index.html
- morecantile: https://developmentseed.org/morecantile/

### Access partitions with a loop
```python
import geopandas  as gpd
import pandas as pd

quadkeys = ["02310101", "02310103"]
year = 2023
month = 11
start_day = 15
end_day = 16

base_url = "s3://us-west-2.opendata.source.coop/root-geospatial/flight-tracks"
part_dfs = []
for quadkey in quadkeys:
    for day in range(start_day, end_day + 1):
        full_url = (
            f"{base_url}/quadkey_z8={quadkey}/year={year}/month={month}/day={day}/"
        )
        part_df = gpd.read_parquet(full_url)
        part_dfs.append(part_df)

df = pd.concat(part_dfs, ignore_index=True)

# remove duplicates where lines overlap multiple tiles
df.drop_duplicates(subset=["icao", "end_t"], inplace=True)

```

### Access partitions with hive filters (slower but more flexible)
```python
import geopandas  as gpd
import pyarrow as pa
import pyarrow.dataset as ds

# Set schema to ensure quadkeys are treated as string, not int
schema = pa.schema([
    ("quadkey_z8", pa.string()),
    ("year", pa.int32()),
    ("month", pa.int8()),
    ("day", pa.int8())
])

quadkeys = ["02310101", "02310103"]
year = 2023
month = 11
start_day = 15
end_day = 16

df = gpd.read_parquet(
    "s3://us-west-2.opendata.source.coop/root-geospatial/flight-tracks/",
    filters=[
        ("quadkey_z8", "in", quadkeys),
        ("year", "==", year),
        ("month", "==", month),
        ("day", ">=", start_day),
        ("day", "<=", end_day)],
    partitioning=ds.HivePartitioning(schema=schema)
)

# remove duplicates where lines overlap multiple tiles
df.drop_duplicates(subset=["icao", "end_t"], inplace=True)

```

### Finding quadkeys covering a bounding box
```python
import morecantile

# bbox in format: xmin, ymin, xmax, ymax
bbox = [-104.7943, 39.7867, -104.3485, 40.0569]

tms = morecantile.tms.get("WebMercatorQuad")
tiles_gen = tms.tiles(*bbox, zooms=[8])
quadkeys = [tms.quadkey(t) for t in tiles_gen]
print(quadkeys)

```

## Limitations
The currently available data is limited to the contiguous United States (CONUS) and includes flights spanning the time period noted in the accompanying [Date Range](https://data.source.coop/root-geospatial/flight-tracks/DATE-RANGE.md) file.

Because the dataset is based on a network of ADS-B ground receivers, flight tracks may be incomplete or missing for any given location and date. Complete coverage of all real-world flights is not guaranteed.

## Citation and Attribution
If you are the author of a publication or research report that uses these data, please include the following citation:

Root Geospatial LLC. 2025. "AeroWhere Flight Tracks: Historical Flight Tracks Dataset". Source Cooperative, https://source.coop/root-geospatial/flight-tracks. Accesed [ACCESS DATE].

For use in web maps, please include the following attribution:
[© AeroWhere by Root Geospatial](https://rootgeo.com/).

## Data Viewer and Exports
A data viewer with functionality for exporting small subset of the data is available at https://aerowhere.com/flight-tracks.

## Source Data
Flight tracks positions and attributes are derived from ADS-B.lol Historical Data: https://www.adsb.lol/docs/open-data/historical/.

Attributes indicating height AGL values are derived from a combination of the altitude values provided in the ADS-B.LOL files and a USGS 3DEP program 1 arc-second DEM (https://www.usgs.gov/3d-elevation-program/about-3dep-products-services).

## Licensing
This data is made available under the Open Database License (ODbL) v1.0.
The full license text is included in the file [LICENSE.txt](https://data.source.coop/root-geospatial/flight-tracks/LICENSE.txt) and can also be found at http://opendatacommons.org/licenses/odbl/1.0/.

extent.spatial.bbox: omitted; spatial reference is categorical via [flights-2023-09-26.geometry (geometry), flights-2023-09-26.quadkey_z8 (spatial_index), flights-2024-10-04.geometry (geometry), flights-2024-10-04.quadkey_z8 (spatial_index), flights-2024-11-11.geometry (geometry), flights-2024-11-11.quadkey_z8 (spatial_index), flights-2025-06-05.geometry (geometry), flights-2025-06-05.quadkey_z8 (spatial_index), flights-2024-12-23.geometry (geometry), flights-2024-12-23.quadkey_z8 (spatial_index), flights-2024-08-10.geometry (geometry), flights-2024-08-10.quadkey_z8 (spatial_index), flights-2023-11-20.geometry (geometry), flights-2023-11-20.quadkey_z8 (spatial_index), flights-2024-01-22.geometry (geometry), flights-2024-01-22.quadkey_z8 (spatial_index), flights-2025-08-08.geometry (geometry), flights-2025-08-08.quadkey_z8 (spatial_index), flights-2024-01-13.geometry (geometry), flights-2024-01-13.quadkey_z8 (spatial_index), flights-2024-12-09.geometry (geometry), flights-2024-12-09.quadkey_z8 (spatial_index), flights-2023-12-27.geometry (geometry), flights-2023-12-27.quadkey_z8 (spatial_index), flights-2024-01-17.geometry (geometry), flights-2024-01-17.quadkey_z8 (spatial_index), flights-2023-07-16.geometry (geometry), flights-2023-07-16.quadkey_z8 (spatial_index), flights-2023-07-03.geometry (geometry), flights-2023-07-03.quadkey_z8 (spatial_index), flights-2023-04-12.geometry (geometry), flights-2023-04-12.quadkey_z8 (spatial_index), flights-2025-03-29.geometry (geometry), flights-2025-03-29.quadkey_z8 (spatial_index), flights-2023-10-25.geometry (geometry), flights-2023-10-25.quadkey_z8 (spatial_index), flights-2025-10-07.geometry (geometry), flights-2025-10-07.quadkey_z8 (spatial_index), flights-2025-04-19.geometry (geometry), flights-2025-04-19.quadkey_z8 (spatial_index), flights-2023-08-24.geometry (geometry), flights-2023-08-24.quadkey_z8 (spatial_index), flights-2024-10-01.geometry (geometry), flights-2024-10-01.quadkey_z8 (spatial_index), flights-2024-10-30.geometry (geometry), flights-2024-10-30.quadkey_z8 (spatial_index), flights-2025-04-28.geometry (geometry), flights-2025-04-28.quadkey_z8 (spatial_index), flights-2024-07-31.geometry (geometry), flights-2024-07-31.quadkey_z8 (spatial_index), flights-2024-08-15.geometry (geometry), flights-2024-08-15.quadkey_z8 (spatial_index), flights-2024-09-18.geometry (geometry), flights-2024-09-18.quadkey_z8 (spatial_index), flights-2025-03-21.geometry (geometry), flights-2025-03-21.quadkey_z8 (spatial_index), flights-2025-09-14.geometry (geometry), flights-2025-09-14.quadkey_z8 (spatial_index), flights-2026-01-02.geometry (geometry), flights-2026-01-02.quadkey_z8 (spatial_index), flights-2023-04-06.geometry (geometry), flights-2023-04-06.quadkey_z8 (spatial_index)]. Add bbox once the placename → bbox gazetteer covers it.


long_lfmc_test

Test upload for our long lfmc dataset.


sentinel1_lfmc

Sentinel-1 based Live Fuel Moisture Content dataset. This is implemented via the method described in Rao et al., 2020, Remote Sensing of Environment: "SAR-enhanced mapping of live fuel moisture content"


Demo for ESIP 2025 Summer Meeting

Demo for ESIP 2025 Summer Meeting


LiveOcean

LiveOcean ROMS Ocean Forecast for Puget Sound


NCEI Estuarine Bathymetry

Gridded bathymetric Digital Elevation Models for 70 estuaries in the conterminous United States, produced by the NOAA National Ocean Service from 150 years of hydrographic survey data. Available as 30-meter resolution NetCDF files with accompanying XML metadata, covering major U.S. estuaries with sufficient historical survey data for detailed bathymetric processing.

bathymetry oceanography topography digital-elevation-model estuaries hydrography hydrology coastal netcdf

NOAA National Centers for Environmental Information (NCEI)

Dataset files (71 x .nc)

Source Cooperative product page

## README.md
# NOAA Estuarine Bathymetry DEMs
The National Ocean Service (NOS) Estuarine Bathymetric Digital Elevation Models are gridded bathymetry datasets interpolated from 150 years’ worth of hydrographic survey data collected by the former NOS Special Projects Office. The initiative produced datasets for 70 estuaries in the conterminous United States with sufficient data coverage to support detailed bathymetric processing.

Estuarine bathymetric data are available as gridded products with 30 meter spatial resolution as NetCDF files, along with XML metadata files.

## Access Data
### Stream Data
Here's an example of how to explore the data interactively in a Jupyter Notebook using Python:
``` python
import fsspec
import xarray as xr
import hvplot.xarray

fs = fsspec.filesystem('s3', anon=True, endpoint_url='https://data.source.coop')
fs.ls('rsignell/ncei-estuarine-bathymetry')

ds = xr.open_dataset(fs.open('rsignell/ncei-estuarine-bathymetry/Buzzards_Bay/buzzards_bay_m010_30m.nc'))
ds['Band1'].hvplot(x='x', y='y', crs='EPSG:26919', rasterize=True, tiles='OSM', cmap='viridis')
```


Sentinel2 Crop labels from ground surveys for six locations in Rwanda

These crop labels were developed from ground surveys at six specific locations in Rwanda in winter (season A) 2021-2022.  The purpose was to compare this method of crop label development against using high resolution drone imagery.  Six major crops were identified: maize, banana, cassava, beans, sweet potatoes, and Irish potatoes.


Sentinel2 crop labels from drone imagery for six locations in Rwanda

These crop labels were developed from drone imagery at six specific locations in Rwanda in winter (season A) 2021-2022.  The purpose was to compare this method of crop label development against using field surveys.  Six major crops were identified: maize, banana, cassava, beans, sweet potatoes, and Irish potatoes.


Rwanda Crop and Land Cover Labels Using Drone Imagery and Field Observations

RTI International (RTI) generated a detailed dataset offering precise crop and land cover classifications across six specific sites in Rwanda. Collected through field observations and high-resolution drone imagery, encompassing 5763 labels.


Overture Maps - Places Dataset - Riyadh, Saudi Arabia

A geospatial dataset of places and areas of interest (AOIs) in Riyadh, Saudi Arabia, containing 32,039 location records derived from Overture Maps in GeoParquet format.

Source Cooperative product page

Dataset files (1 × .parquet)

Overture Maps data terms; consult source.coop for licensing details


test-1


A collection of ecological model outputs — primarily species distribution models — for Antarctica and the Southern Ocean, compiled by the SCAR Expert Group on Biodiversity Informatics. Outputs are provided as cloud-optimized GeoTIFFs (one file per species, with multiple bands where uncertainty estimates or multiple output variables are included), each retained in its original coordinate reference system and spatial resolution. The collection covers circumpolar and regional domains across the Antarctic continent and Southern Ocean.

southern-ocean species-distribution-model remote-sensing biodiversity ecology marine oceanography

Dataset files (418 x .tif)

Source Cooperative product page

## README.md
# DistAnt

## Overview

This repository provides access to a collection
of ecological model outputs (species distribution and similar models)
from Antarctica and the Southern Ocean.
It is a project of the SCAR [Expert Group on
Biodiversity Informatics](https://scar.org/science/egabi/home/) in
conjunction with
[ADVANCE](https://www.belspo.be/belspo/impuls/project_en.stm#ADVANCE)
(Royal Belgian Institute of Natural Sciences) and the Integrated Digital
East Antarctica program at the [Australian Antarctic
Division](https://www.antarctica.gov.au/science/).

**Please note:** the inclusion of a layer in this collection is not an
endorsement of its quality or suitability for your intended purpose.
Users should consult the associated publication for details on the
source data and modelling processes. We encourage users to contact the
original publication authors to discuss their intended use of these
layers.

Model outputs are provided as cloud-optimized geotiffs (COGs), with
generally one file per species. The COG will have multiple bands if the
original model predictions include uncertainty estimates or multiple
model output variables. Each output has been kept on its original
coordinate reference system (map projection) and spatial resolution.

### Citing

The layers in this collection have been re-released under their original
license where applicable, or a CC-BY licence otherwise. Please cite when
using, and also cite the original data sources used. For example:

> Kovacs J (2020) A model of my favourite Southern Ocean species. *Journal of Southern Ocean Stuff* **123**:1-10. Data obtained from the SCAR DistAnt Ecological Model Output Repository, 10.5281/zenodo.10910075

Individual data sources might have varying licence conditions: consult
the `licence` field for details.

## Contribute data

If you have generated some model outputs that would be suitable for this
collection, or are aware of some that we haven’t yet included, please
[open an issue and let us
know](https://github.com/SCAR/distant/issues/new/choose).

The focus for this repository --- at least initially --- is on circumpolar
layers that encompass the whole Southern Ocean or whole Antarctic
continent. However, regional outputs that cover smaller domains will be
considered, particularly if they relate to a taxon or output variable for
which there is no circumpolar output.

## Project contributors

  - Charlie Plasman, Anton Van de Putte (Royal Belgian Institute of
    Natural Sciences, Université Libre de Bruxelles)
  - Salomé Fabri-Ruiz (Institut Français de Recherche pour
    l’Exploitation de la Mer)
  - Jennifer Freer, Huw Griffiths (British Antarctic Survey)
  - David Green, Benjamin Viola, Briannyn Woods (Institute for Marine
    and Antarctic Studies)
  - Jean-Olivier Irisson (Institut de la Mer de Villefranche)
  - Lucas Krüger (Instituto Antártico Chileno)
  - Benjamin Merkel (Norwegian Polar Institute)
  - Matt Pinkerton (National Institute of Water and Atmospheric
    Research)
  - Ryan Reisinger (University of Southampton)
  - Giovanni Testa, Andrea Piñones (Centro de Investigación en Dinámica
    de Ecosistemas Marinos de Altas Latitudes)
  - Anikó Tóth (University of New South Wales)
  - José Xavier (University of Coimbra, British Antarctic Survey)
  - Ben Raymond, Aleks Terauds (Australian Antarctic Division)

## Contents

The `metadata.csv` file provides a table of minimal metadata that describes the
layers in the collection. This metadata is intended to be sufficient for
users to find potential layers of interest, and make an initial
evaluation of their high-level characteristics such as taxonomic
details, spatial coverage and resolution, and model output types. Our
metadata is NOT intended to provide a comprehensive description of each
layer: users are referred to the original publication for that level of
detail (see the layer’s `reference` entry).

license: Layers are re-released under their original license where applicable, or CC-BY otherwise; individual items may have varying conditions — consult the 'licence' field in metadata.csv.

providers: role [processor, host] for 'SCAR Expert Group on Biodiversity Informatics' is an AI guess — Compiles and republishes model outputs from multiple original authors; acts as aggregator/processor rather than primary producer.. Verify before flipping to _state: reviewed.

providers: role [producer] for 'Royal Belgian Institute of Natural Sciences (ADVANCE / RBINS)' is an AI guess — Listed as a project partner and contributor; assumed co-producer of some model outputs.. Verify before flipping to _state: reviewed.

providers: role [producer] for 'Australian Antarctic Division' is an AI guess — Listed as a project partner via the Integrated Digital East Antarctica program; assumed co-producer of some model outputs.. Verify before flipping to _state: reviewed.


HIFLD Archive

An archive of the U.S. Homeland Infrastructure Foundation-Level Data (HIFLD) program, preserving hundreds of federal infrastructure datasets discontinued in August 2025. Coverage spans administrative boundaries, hydrography, transportation networks, critical infrastructure facilities, wildfire perimeters, and maritime wrecks across the United States and territories. All datasets have been converted to cloud-optimized Parquet format and spatially sorted by geohash level 8.

critical-infrastructure administrative-boundaries hydrology wildfire conservation maritime emergency-management homeland-security

Homeland Infrastructure Foundation-Level Data (HIFLD) Program

CENTLAT

[latlon] Centroid latitude of congressional district

CENTLON

[latlon] Centroid longitude of congressional district

GEOID

[admin_code] Census GEOID for the district

INTPTLAT

[latlon] Interior point latitude of congressional district

INTPTLON

[latlon] Interior point longitude of congressional district

STATE

[admin_code] State code for the congressional district

geometry

[geometry] Congressional district boundary polygon

bbox

STRUCT(xmin DOUBLE, ymin DOUBLE, xmax DOUBLE, ymax DOUBLE)

geometry

[geometry] Catchment polygon boundary

[geometry] Flowline linear geometry

STRUCT(county VARCHAR, date DOUBLE, job_date BIGINT, state VARCHAR)

[geometry] Hydrolocation point geometry

properties

STRUCT(OBJECTID BIGINT, featuredate BIGINT, featuretype BIGINT, featuretypelabel VARCHAR, gnisid VARCHAR, gnisidlabel VARCHAR, id3dhp VARCHAR, mainstemid VARCHAR, universalreferenceid VARCHAR, workunitid VARCHAR)

2 files sampled for this table

geometry

[geometry] Watershed boundary line geometry

[latlon] Latitude of fire discovery point

[latlon] Longitude of fire discovery point

[placename] City of point of origin for the fire

[placename] County of point of origin for the fire

[admin_code] FIPS code of the point-of-origin county

[placename] State of point of origin for the fire

[geometry] Wildfire perimeter polygon

[geometry] Wreck area polygon geometry

[geometry] Wreck or obstruction geometry

[geometry] Wreck point geometry

Source Cooperative product page

## README.md
# Homeland Infrastructure Foundation-Level Data (HIFLD) Archive

The Homeland Infrastructure Foundation-Level Data (HIFLD) program was a trusted resource for infrastructure data across the U.S. It supported policy, emergency management, and research before being discontinued in August 2025. This archive preserves that resource.

The datasets were processed using SeerAI Boson, Databricks, and Apache Sedona and saved in the Parquet format for ease of access directly on cloud object storage. If a dataset included geometry, it was spatially sorted using geohash level 8, which was then removed from the dataset.

## Dataset Directory Structure
Many of the original datasets contained multiple collections, and each collection was archived as its own parquet file. To preserve some of the original structure, there is a nested folder hierarchy for datasets and files. Please see below for the structure.

```
HIFLD
├── README.md                                       -> This file
├── hifld-dataset-inventory.csv                     -> Inventory of all datasets, their descriptions, any parent dataset folder, bucket location, and URL to vector tile JSON
├── 119th-congressional-districts                   -> Dataset parent folder
│   ├── README.md                                   -> Parent dataset README
│   ├── 119th-congressional-districts               -> Dataset folder
│   │   ├── 119th-congressional-districts.parquet   -> Dataset parquet
│   │   ├── README.md                               -> Dataset-specific README
├── 3d-hydrography-program-3dhp-layers              -> Dataset parent folder
│   ├── README.md                                   -> Parent dataset README
│   ├── catchment                                   -> Dataset folder - corresponding to collection in original dataset
│   │   ├── catchment.parquet                       -> Dataset parquet
│   │   ├── README.md                               -> Dataset-specific README
│   ├── flowline
│   │   ├── flowline.parquet
│   │   └── README.md
│   ├── ...
├── ...
```

## License
The HIFLD archive provided by SeerAI is released under the Creative Commons Attribution 4.0 International (CC BY 4.0).

## Citation
SeerAI, Inc. (2025). HIFLD Archive. Retrieved from https://www.seer.ai/

## Contact
For questions, issues, or feedback regarding this archive, please contact us at [contact@seerai.space](mailto:contact@seerai.space).


Apache Parquet Testing Data

This product contains a subset of the Parquet files published in https://github.com/apache/parquet-testing. It includes both correct (data) and bad (bad_data) Parquet files. The data/geospatial subdirectory contains test files for the new GEOMETRY logical type.


CSV PapaParse test files

The CSV files used to test the PapaParse library.


AMX Project 2024-04 PMTiles

The AMX (Adopt Map XML) Project converts open cadastral map data released by the Japanese Ministry of Justice into modern web formats. This dataset is derived from the 3rd release of 2024-04 and distributed as a single PMTiles archive covering Japan. It enables browser-based vector tile rendering of official Japanese land parcel map data.

land-parcels cadastral pmtiles ministry-of-justice

Dataset files (1 x .pmtiles)

Source Cooperative product page

providers: role [processor] for 'smartmaps' is an AI guess — Assumed processor role based on dataset being hosted under the 'smartmaps' account and described as produced from the Ministry of Justice source data.. Verify before flipping to _state: reviewed.


Japan GSI 1m GSD Terrain Tiles in PMTiles

High-resolution terrain tiles for Japan at 1 meter ground sample distance (GSD), produced by the Geospatial Information Authority of Japan (GSI) and packaged as a single PMTiles archive. Covers Japan's terrain with elevation data derived from official government survey sources.

topography elevation digital-elevation-model pmtiles remote-sensing

Geospatial Information Authority of Japan (GSI / 国土地理院)

Dataset files (1 x .pmtiles)

Source Cooperative product page


High-resolution 1-meter mesh Digital Elevation Model (DEM) tiles for Japan, derived from the Geospatial Information Authority of Japan's (GSI) Foundation Map Information dataset. Distributed as a single PMTiles archive for web-based terrain visualization and elevation analysis.

elevation topography digital-elevation-model pmtiles

Geospatial Information Authority of Japan (国土地理院)

Dataset files (1 x .pmtiles)

Source Cooperative product page

## README.md
# dem1a: PMTiles terrain tiles from dem1a by Geospatial Information Authority of Japan
## Demo

https://observablehq.com/d/c0a3f48cf7111cd2

## Source

基盤地図情報（数値標高モデル）１ｍメッシュ（標高）

測量法に基づく国土地理院長承認（使用）R 6JHs 133


FOIL4G Data Pack Release 1

FOIL4G (Free and Open Information Library for Geospatial) Data Pack Release 1 is a collection of seven global-extent PMTiles files covering the full Web Mercator range. The pack totals approximately 25 GB and appears to be a curated release of open geospatial tile data.

Dataset files (7 x .pmtiles)

Source Cooperative product page

providers: role [producer, licensor] for 'FOIL4G' is an AI guess — No README; assumed producer/licensor from the project name (Free and Open Information Library for Geospatial). Verify before flipping to _state: reviewed.


Smart Maps Global Elevation Tiles

Global RGB elevation tiles derived from NASADEM (zoom levels 6–12) and Global Map/ISCGM data (zoom levels 2–5), packaged as a single PMTiles file using Mapbox RGB pixel-packing (WebP). The dataset covers nearly the entire globe and is released as open data to reduce copyright constraints on terrain visualization. Produced under the UN Smart Maps Group of the UN Open GIS Initiative.

elevation topography digital-elevation-model pmtiles remote-sensing global-coverage raster-tiles

ISCGM / National Geospatial Information Authorities (Global Map)

Dataset files (1 x .pmtiles)

Source Cooperative product page

## README.md
## Overview

This dataset is RGB elevation tiles of the world converted from open data namely NASADEM (zoom level: 6-12) and Global Map data (zoom level: 2-5). Although we can find RGB elevation tiles dataset easily, sometimes it is difficult to use it because of the copyright. We made these RGB elevation tiles as open data to reduce constraints in the importation of geographical data and make the imported data flexible and effectively usable anywhere. This work is done under the UN Smart Maps Group of UN Open GIS Initiative.

## Demo: See it in action

- [A demo on Observable](https://observablehq.com/d/652ef8ca26776146) for a quick overview of the dataset.
- [A fullscreen website](https://optgeo.github.io/glob/) with hash URL enabled.

## Original datasets

- NASADEM

NASADEM is based on SRTM and complemented by ASTER G-DEM and ALOS 30 meter DEM. It has 1 arc second spatial resolutions (about 30 meters).

https://lpdaac.usgs.gov/products/nasadem_hgtv001/

- Global Map (ISCGM)

Global Map is fundamental digital geospatial information covering the whole land of the globe. Global Map data were developed under the cooperation of National Geospatial Information Authorities (NGIAs) of respective countries and regions. ISCGM served as the secretariat until the project concluded in 2017.

https://globalmaps.github.io/

## Data

Format: PMTiles

Zoom level: 6 to 12

Spatial Extent:
- ZL 2-5 Global (around 85°N to 85°S, 180°W to 180°E)
- ZL 6-12 Global (60°N to 56°S, 180°W to 180°E)

Size: 182.5 GB (The number of file: 1)

Convert Date: 2022 October

URL: https://data.source.coop/smartmaps/gel/gel.pmtiles

## Dataset License

CC0(No Rights Reserved)

https://creativecommons.org/public-domain/cc0/

## Reference

- Technical Detail

*https://qiita.com/T-ubu/items/c7bb5697b51f245ef163*

*https://qiita.com/T-ubu/items/ced8423e0ebeda8c9244*

- UN Smart Maps Group website (on GitHub Pages)

https://github.com/UNopenGIS/7

- UN Open GIS Initiative website

http://unopengis.org/unopengis/main/main.php

providers: role [processor] for 'UN Smart Maps Group / UN Open GIS Initiative' is an AI guess — README credits this group with converting the source DEMs to RGB PMTiles; assumed processor role.. Verify before flipping to _state: reviewed.


H3YS WorldPop

WorldPop unconstrained population counts for individual countries from 2000 to 2020, originally at 100m resolution, reaggregated into H3 hexagonal cells and distributed as cloud-optimized PMTiles. Part of the UN Open GIS Initiative Smart Maps Portable Web project for lightweight, portable web mapping.

population-counts demographics worldpop h3 pmtiles temporal-series

Dataset files (11 x .pmtiles)

Source Cooperative product page

## README.md
# h3ys-worldpop
WorldPop Population Counts / Unconstrained individual countries 2000-2020
(originally 100m resolution) aggregated in H3 and packed in PMTiles.

This is a UN Open GIS Initiative Smart Maps Portable Web project.

# Demo
https://hfu.github.io/worldpop-h3

# Development
https://github.com/hfu/worldpop-h3

# Issue and Todo tracking
https://github.com/unopengis/7/issues/652

# See Also
https://hub.worldpop.org/geodata/listing?id=29


Japan GeoTIFF DEM for Mapterhorn

Digital Elevation Model (DEM) data for Japan sourced from the Geospatial Information Authority of Japan (GSI), converted to GeoTIFF format at 10m, 5m, and 1m resolutions. Coverage spans the Japanese archipelago (approximately 26°N–45°N, 128°E–142°E) across nearly 645,000 tiles organized by resolution tier.

digital-elevation-model topography elevation earth-observation

Dataset files (644989 x .tif)

Source Cooperative product page

## README.md
# Japan GeoTIFF DEM Utility for Mapterhorn

## Overview

This repository provides utilities to work with the Japan Geospatial Information Authority’s Digital Elevation Model (DEM) data, available at 10m, 5m, and 1m resolutions, converted into GeoTIFF format.

## Features

- Simplifies working with official DEM data of Japan
- Multiple resolutions (1m, 5m, 10m)
- Ready to use with various mapping tools

## Contents
- 10/ - 10m resolution DEM
- 5/ - 5m resolution DEM
- 1/ - 1m resolution DEM

For additional details, see: [optgeo/japan-geotiff-dem](https://github.com/optgeo/japan-geotiff-dem)

## License

See below: approval and copyright.

## Clarifications

測量法に基づく国土地理院長承認（複製）R8JHf51

不特定多数の者が提供を受けることができる状態に置く措置をとるために本製品を複製する場合には、国土地理院の長の承認を得なければなりません。

Approval for Reproduction pursuant to the Survey Act, granted by the Director-General of the Geospatial Information Authority of Japan (R8JHf51).

When reproducing this product in order to make it available to the general public, approval from the Director-General of the Geospatial Information Authority of Japan must be obtained.

## Links

- Source Cooperative "smartmaps/japan-geotiff-dem"
  https://source.coop/smartmaps/japan-geotiff-dem

- GitHub "optgeo/japan-geotiff-dem"
  https://github.com/optgeo/japan-geotiff-dem

## Changelog

- 2026-05-28: First complete upload done.

providers: role [processor] for 'optgeo' is an AI guess — Assumed processor role based on GitHub project optgeo/japan-geotiff-dem, which converted original GSI DEM source data to GeoTIFF format.. Verify before flipping to _state: reviewed.


Japan GSI seamlessphoto

Complete nationwide aerial imagery of Japan at maximum resolution (Zoom 18), sourced from the Geospatial Information Authority of Japan (GSI) seamlessphoto dataset. The full country extent is packaged as a PMTiles archive optimized for efficient web-based distribution and tile-based processing.

aerial-imagery pmtiles remote-sensing earth-observation

Dataset files (2 x .pmtiles)

Source Cooperative product page

providers: role [producer, licensor] for 'Geospatial Information Authority of Japan (GSI)' is an AI guess — GSI is explicitly named as the source of the seamlessphoto dataset; licensor role assumed as the originating government authority since no separate license terms are stated.. Verify before flipping to _state: reviewed.


Mobility Data GTFS PMTiles

A global PMTiles vector tile file derived from all active GTFS (General Transit Feed Specification) feeds in the Mobility Database Catalog, maintained by Mobility Data. It consolidates public transit network data—stops, routes, and shapes—from transit agencies worldwide into a single tileset. The dataset is described as a work in progress with planned updates.

public-transit gtfs pmtiles vector-tiles transit-networks mobility

Dataset files (1 x .pmtiles)

Source Cooperative product page

## README.md
# Mobility Data GTFS PMTiles

A single PMTiles file created from all active GTFS data from Mobility Database Catalog from Mobility Data.

This is a work in progress. Data will be updated.

## Demo
https://protomaps.github.io/PMTiles/?url=https%3A%2F%2Fdata.source.coop%2Fsmartmaps%2Fmobility-gtfs-pmtiles%2Fmobility.pmtiles

## Specifications
Downloading and conversion script is at https://github.com/optgeo/mmx-gtfs.

## See also
- https://mobilitydatabase.org/

providers: role [producer] for 'Mobility Data' is an AI guess — Operates the Mobility Database Catalog that aggregates GTFS feeds; underlying feeds are from individual transit agencies not separately attributed in the README.. Verify before flipping to _state: reviewed.

providers: role [processor] for 'optgeo' is an AI guess — Assumed processor based on authorship of the mmx-gtfs download and conversion script linked in the README.. Verify before flipping to _state: reviewed.


Nagasaki LiDAR 3D Tiles (Ver. 2024-05)

LiDAR-derived 3D point cloud dataset covering a section of Nagasaki, Japan, delivered in the 3D Tiles .pnts format. The dataset contains over 2,100 point cloud tile files spanning the geographic extent of the surveyed area. Version dated May 2024.

lidar point-cloud 3d-tiles urban-mapping building-footprints

Dataset files (2142 x .pnts)

Source Cooperative product page

license: AI returned 'unknown' (README does not state a license). Verify with publisher before flipping to _state: reviewed.


国土数値情報サンプル

Sample files from Japan's National Land Numerical Information (国土数値情報, KSJ) published by Japan's Ministry of Land, Infrastructure, Transport and Tourism (MLIT). Includes 2024 land price survey points, 2024 administrative boundary polygons, and 2006–2009 river line data covering Japan. Distributed in PMTiles and FlatGeobuf formats.

administrative-boundaries land-price rivers hydrology national-land-information open-government-data

Japan Ministry of Land, Infrastructure, Transport and Tourism (MLIT) GIS Lab

Dataset files (3 x .fgb)

Source Cooperative product page

## README.md
# next-ksj
https://github.com/gislab-mlit/next-ksj-formats で公開されているサンプルファイルのコピーです。
PMTiles 形式と FlatGeobuf 形式のファイルのコピーを置いています。

# pmtiles.io による閲覧
## L01-24_地価公示データ_2024年(令和6年)版
- https://pmtiles.io/?url=https%3A%2F%2Fdata.source.coop%2Fsmartmaps%2Fnext-ksj%2FL01-24.pmtiles#map=11.64/35.6818/139.7465

## N03-20240101_行政区域データ_2024年(令和6年)版 （ポリゴン）
- https://pmtiles.io/?url=https%3A%2F%2Fdata.source.coop%2Fsmartmaps%2Fnext-ksj%2FN03-20240101.pmtiles#map=4.21/33.91/138.46

## W05_河川データ_2009年度～2006年度(平成21年度～平成18年度) （ライン）
- https://pmtiles.io/?url=https%3A%2F%2Fdata.source.coop%2Fsmartmaps%2Fnext-ksj%2FW05-Stream.pmtiles#map=9.64/35.7108/139.8437

# See Also
https://github.com/gislab-mlit/next-ksj-formats/issues/18


Smart Maps LiDAR 3D Tiles from Open Nagasaki

A 3D Tiles point cloud dataset assembled by the UN Smart Maps Group from all publicly available LiDAR data published through Open Nagasaki (opennagasaki.nerc.or.jp). The dataset covers the Nagasaki prefecture region of Japan and is served in the .pnts (3D Tiles Point Cloud) format, comprising over 1.7 million tiles totalling approximately 232 GB.

Dataset files (1715716 x .pnts)

Source Cooperative product page

providers: role [producer, licensor] for 'Open Nagasaki (NERC)' is an AI guess — Original LiDAR data source; licensor assumed as the originating open-data portal. Verify before flipping to _state: reviewed.


OpenCellid PMTiles - FOIL4G (Free and Open Information Library for Geospatial)

OpenCelliD is the world's largest collaborative community project collecting GPS positions of cell towers globally. This dataset packages the full OpenCelliD cell tower database as a PMTiles file for efficient spatial tile access. It covers cell tower locations worldwide across multiple radio access technologies and network operators.

cell-towers telecommunications mobile-networks crowd-sourced coverage-mapping pmtiles

Dataset files (1 x .pmtiles)

Source Cooperative product page

providers: role [processor] for 'FOIL4G' is an AI guess — Assumed processor role based on the dataset title crediting FOIL4G for the PMTiles conversion of the original CSV source. Verify before flipping to _state: reviewed.


rw-wip-2024-05-31


Terrain tiles in PMTiles from Dem_Rwanda_10m_allt_20230921150153_band1.tif

Terrain tiles in PMTiles from https://geohub.data.undp.org/data/00d5add9be37e465398b081683c3ec03#Info. The original data is by Water and Sanitation Corporation (WASAC), National Land Authority (NLA).


Study on Strategic Use of Geospatial Information; support for JICA

A UN Smart Maps Group repository in support of JICA Study on Strategic Use of Geospatial Information (2025-04/2025-11)


Topographic Maps of East Timor (TMET)

This is an old topographic map dataset for East Timur created with UNTAET.

JICA and UNTAET agreed that the digital topographic maps and digital data for GIS to be prepared by the Study on Urgent Establishment of Topographic Mapping East Timor will be open to the pubic use.

However, this repository is not currently intended for public release of the data.


都市計画決定PMTiles

Urban planning decision GIS data from Japan's Ministry of Land, Infrastructure, Transport and Tourism (MLIT), covering 21 urban planning zone categories including land use zones (用途地域), urban planning areas, road networks, parks, and fire prevention districts. Derived from official MLIT sources and served as a single PMTiles vector tile file spanning the Japanese archipelago.

Ministry of Land, Infrastructure, Transport and Tourism (MLIT), Japan

Dataset files (1 x .pmtiles)

Source Cooperative product page

## README.md
# tk
都市計画決定GISデータ（国土交通省）

## Demo
- [PMTiles Viewer](https://protomaps.github.io/PMTiles/?url=https%3A%2F%2Fdata.source.coop%2Fsmartmaps%2Ftoshik%2Fa.pmtiles#map=6.92/35.722/138.52)

## Source Specifications
https://www.mlit.go.jp/toshi/tosiko/content/001609328.pdf

## basic schema
- code - YoutoID (youto), youtoID (tkbt), and kubunID unified.
- name - 名称 and tokeiname unified.
- date - 決定日
- status - 決定区分
- by - 決定者
- no - 告示番号
- city - citycode (5桁)

## layers
1. youto 用途地域
2. tokei 都市計画区域
3. senbiki 区域区分
4. ritteki 立地適正化計画
5. douro 都市計画道路
6. bouka 防火準防火
7. koudoti 高度地区
8. koudori 高度利用地区
9. tochiku 土地区画整理事業
10. chikukei 地区計画
11. tkbt 特別用途地区
12. kouen 公園
13. tokureiyouseki 特例容積率適用地区
14. kousoujyukyo 高層住居誘導地区
15. kyojyuchosei 居住調整地域
16. tokuteiyuudou 特定用途誘導地区
17. tokuteibouka 特定防火街区整備地区
18. rekifuu 歴史的風土保存地区、歴史的風土特別保存地区
19. ryokukachiiki 緑化地域
20. soubou 航空機騒音障害防止地区、航空機騒音障害防止特別地区
21. fukkousaiseikyoten 一団地の復興再生拠点市街地形成施設

## code list
### youto
- 1 第１種低層住居専用地域
- 2 第２種低層住居専用地域
- 3 第１種中高層住居専用地域
- 4 第２種中高層住居専用地域
- 5 第１種住居地域
- 6 第２種住居地域
- 7 準住居地域
- 8 田園住居地域
- 9 近隣商業地域
- 10 商業地域
- 11 準工業地域
- 12 工業地域
- 13 工業専用地域

### senbiki
- 22 市街化区域
- 23 市街化調整区域

### ritteki
- 31 居住誘導区域
- 32 都市機能誘導区域

### douro
- 3011 道路
- 3023 広場

### bouka
- 24 防火地域
- 25 準防火地域

### tkbt
- 1 特別工業地区
- 2 文教地区
- 3 小売店舗地区
- 4 事務所地区
- 5 厚生地区
- 6 娯楽レクリエーション地区
- 7 観光地区
- 8 特別業務地区
- 9 中高層階住居専用地区
- 10 商業専用地区
- 11 研究開発地区

### kouen
- 3021 公園
- 3022 緑地
- 3024 墓園

### soubou
- 40 航空機騒音障害防止地区
- 41 航空機騒音障害防止特別地区

## attribution
[都市計画決定GISデータ（国土交通省）](https://www.mlit.go.jp/toshi/tosiko/toshi_tosiko_tk_000087.html)を加工して使用

## Data repository
[Source Cooperative smartmaps/toshik](https://beta.source.coop/repositories/smartmaps/toshik)

providers: role [processor] for 'smartmaps' is an AI guess — Inferred as processor from attribution stating MLIT data was processed (加工して使用); smartmaps publishes the converted PMTiles.. Verify before flipping to _state: reviewed.


PMTiles of UCDP (Uppsala Conflict Data Program) Georeferenced Event Dataset (GED) Global ver. 23.1

PMTiles vector tileset of the UCDP (Uppsala Conflict Data Program) Georeferenced Event Dataset (GED) Global version 23.1. The underlying dataset records individual organized violence events (state-based, non-state, and one-sided) worldwide from 1989 through 2022, each georeferenced to a point location. This tileset packages the data for efficient web-map delivery using the PMTiles archive format.

armed-conflict conflict-events political-violence organized-violence ucdp

Uppsala Conflict Data Program (UCDP), Uppsala University

Dataset files (1 x .pmtiles)

Source Cooperative product page


Vector Tiles (MVT) and 3D Tiles (b3dm) derived from Japan's Project PLATEAU 3D urban city models, covering areas from approximately 127°E–141°E, 26°N–43°N across Japan. The dataset conforms to Project PLATEAU Standard Product Specification V3 and contains nearly 2.5 million 3D model tiles alongside over 1.1 million vector map tiles.

Project PLATEAU / Ministry of Land, Infrastructure, Transport and Tourism (MLIT), Japan

Dataset files (2497447 x .b3dm)

Source Cooperative product page

## README.md
# xing: Vector Tiles and 3D Tiles from Project PLATEAU

## Source
[3D都市モデル（Project PLATEAU）ポータルサイト](https://www.geospatial.jp/ckan/dataset/plateau)

- 標準製品仕様書V3に対応したデータのみを収録することにします。


NASA CMR Metadata

No Description Provided


Source Cooperative Manifests

No Description Provided


sites.source.coop

Content for sites.source.coop landing page


Source Stats

Weekly CSVs with statistics about data hosted in Source Cooperative


USDA County Boundaries (Cloud-Native Geo Distribution)

USDA County Boundaries is the official county-level boundary file of the USDA (United States Department of Agriculture) Census of Agriculture 2017. This distribution of the dataset makes it available in cloud-native geospatial format (GeoParquet), and offers some additional transformations and processing to increase utility for agricultural modelling.

vector agriculture geoparquet usda county state


ESA WorldCereal

The ESA WorldCereal dataset provides farm point locations derived from 10-meter resolution global crop type classification maps, covering maize, winter cereals, and spring cereals. Points are partitioned by administrative boundaries (levels 0 and 1) and distributed in cloud-native GeoParquet format. The dataset contains nearly 7 million features spanning a near-global extent.

agriculture crop-classification remote-sensing maize cereals global-coverage arable-land land-use

Dataset files (1 x .geoparquet)

Source Cooperative product page


GDELT-SA

Daily-updated Parquet dataset of GDELT 2.0 geopolitical event records filtered to Saudi Arabia, partitioned by calendar day. Each row represents a news-reported event with GDELT actor and action codes, Goldstein scale scores, and sentiment metrics across three geographic reference frames (Actor1, Actor2, Action location). Records are deduplicated by source URL and pipeline-enriched with nearest-city geographic matching and optionally scraped article full text.

time-series event-data media-monitoring sentiment-analysis middle-east news-events conflict-monitoring

2026_01_01.parquet

[placename] Full place name associated with Actor 1

[admin_code] FIPS ADM1 code for Actor 1 location

[latlon] Latitude of Actor 1 geographic location (paired with Actor1Geo_Long)

[latlon] Longitude of Actor 1 geographic location (paired with Actor1Geo_Lat)

[placename] Full place name associated with Actor 2

[admin_code] FIPS ADM1 code for Actor 2 location

[latlon] Latitude of Actor 2 geographic location (paired with Actor2Geo_Long)

[latlon] Longitude of Actor 2 geographic location (paired with Actor2Geo_Lat)

[placename] Full place name of the event action location

[admin_code] FIPS country code for the action location

[admin_code] FIPS ADM1 (first-level administrative division) code for the action location

[latlon] Latitude of the event action location (paired with ActionGeo_Long)

[latlon] Longitude of the event action location (paired with ActionGeo_Lat)

NearestCity

[placename] Pipeline-enriched nearest city name matched to ActionGeo coordinates via Haversine distance

[placename] Pipeline-enriched nearest city name matched to Actor1 coordinates

[placename] Pipeline-enriched nearest city name matched to Actor2 coordinates

2026_01_02.parquet

2026_01_03.parquet

2026_01_04.parquet

2026_01_05.parquet

2026_06_13.parquet

2026_06_14.parquet

2026_06_15.parquet

2026_06_16.parquet

2026_06_17.parquet

Source Cooperative product page

## README.md
# GDELT Saudi Arabia Daily News Events

Enriched [GDELT 2.0](https://www.gdeltproject.org/) event records related to Saudi Arabia, updated daily. Each record is deduplicated, geo-enriched with nearest-city matching, and includes scraped article full text when available.

**Demo:** [geonews.tabaqat.dev](https://geonews.tabaqat.dev/) - Interactive map to explore the data.

## Bucket Structure

```
s3://us-west-2.opendata.source.coop/tabaqat/gdelt-sa/
└── country=SA/
    └── year=YYYY/
        ├── YYYY_MM_DD.parquet
        ├── YYYY_MM_DD.parquet
        └── ...
```

One Parquet file per day (ZSTD compressed). New files are synced daily around midnight UTC via GitHub Actions.

## What's in Each File

Every row is a single GDELT event. Columns fall into four groups:

| Group                | Examples                                                                                | Count |
| -------------------- | ---------------------------------------------------------------------------------------------------------------------- | ----- |
| **Event**            | `GLOBALEVENTID`, `SQLDATE`, `EventCode`, `QuadClass`, `GoldsteinScale`, `AvgTone`, `NumMentions`                       | 34    |
| **Actor geography**  | `Actor1Geo_FullName`, `Actor1Geo_Lat/Long`, `Actor2Geo_*`                                                              | 16    |
| **Action geography** | `ActionGeo_FullName`, `ActionGeo_CountryCode`, `ActionGeo_Lat/Long`                                                    | 8     |
| **Geo enrichment**   | `NearestCity`, `CityPopulation`, `DistanceToCity_km`, `CoordQuality` (×3 geo sets)                                     | 12    |
| **Article**          | `ArticleTitle`, `ArticleContent`, `ArticleAuthor`, `ArticlePublishDate`, `ArticleContentLength`, `ArticleScrapeMethod` | 6     |

**~76 columns total.** Full schema details in the [source repo](https://github.com/tabaqatdev/gdelt-cng).

## Processing Pipeline

Raw GDELT 15-minute dumps go through:

1. **Filter** — keep only events involving Saudi Arabia
2. **Clean** — remove nulls, empty URLs, invalid tone values
3. **Deduplicate** — one best record per source URL (scored by data completeness)
4. **Geo-enrich** — match coordinates to nearest city using a 33K+ world cities reference via Haversine distance
5. **Scrape** — extract article text using Trafilatura → Newspaper4k → Playwright fallback chain (up to 500/day)

## Quick Query (DuckDB)

```sql
-- read a single day
SELECT * FROM 's3://us-west-2.opendata.source.coop/tabaqat/gdelt-sa/country=SA/year=2026/2026_01_01.parquet' LIMIT 10;

-- count events with articles across all days
SELECT count(*) AS total,
       count(ArticleTitle) AS with_articles
FROM 's3://us-west-2.opendata.source.coop/tabaqat/gdelt-sa/country=SA/**/*.parquet';

-- top mentioned cities
SELECT NearestCity, count(*) AS n
FROM 's3://us-west-2.opendata.source.coop/tabaqat/gdelt-sa/country=SA/**/*.parquet'
WHERE NearestCity IS NOT NULL
GROUP BY 1 ORDER BY 2 DESC LIMIT 10;
```

## License & Attribution

Data derived from the [GDELT Project](https://www.gdeltproject.org/). Refer to GDELT's terms of use for redistribution rights. Pipeline source code is MIT licensed.

extent.spatial.bbox: omitted; spatial reference is categorical via [01.ActionGeo_Lat (latlon), 01.ActionGeo_Long (latlon), 01.ActionGeo_FullName (placename), 01.ActionGeo_CountryCode (admin_code), 01.ActionGeo_ADM1Code (admin_code), 01.Actor1Geo_Lat (latlon), 01.Actor1Geo_Long (latlon), 01.Actor1Geo_FullName (placename), 01.Actor1Geo_ADM1Code (admin_code), 01.Actor2Geo_Lat (latlon), 01.Actor2Geo_Long (latlon), 01.Actor2Geo_FullName (placename), 01.Actor2Geo_ADM1Code (admin_code), 01.NearestCity (placename), 01.Actor1_NearestCity (placename), 01.Actor2_NearestCity (placename)]. Add bbox once the placename → bbox gazetteer covers it.


geocoding-cng

A geocoding dataset for Saudi Arabia containing over 35,000 address-level records organized into H3 spatial index tiles, covering street addresses with full administrative hierarchy (district, governorate, region) in both Arabic and English. Includes companion polygon boundary layers for Saudi regions, governorates, and districts, plus a postcode index and a world countries reference layer.

geocoding addresses administrative-boundaries postcode h3 arabic

postcode_index.parquet

postcode

[zipcode] Saudi postal code used as lookup key for H3 tile list

[placename] Region name in Arabic

[placename] Region name in English

region_index.parquet

[placename] Region name in Arabic with bounding box

sa_districts_simple.parquet

city

[placename] City the district belongs to

centroid

[geometry] Centroid point of the district polygon

geometry

[geometry] Polygon boundary of the district

sa_governorates_simple.parquet

[geometry] Polygon boundary of the governorate

centroid

[geometry] Centroid point of the governorate polygon

sa_regions_simple.parquet

[geometry] Centroid point of the region polygon

geometry

[geometry] Polygon boundary of the region

8553ad1bfffffff.parquet

longitude

[latlon] Address longitude (representative tile; same schema applies to all H3-named tiles)

latitude

[latlon] Address latitude

postcode

[zipcode] Postal code of the address

[placename] District name in Arabic

city

[placename] City name

[placename] Region name in Arabic

[spatial_index] H3 cell index identifying the tile this address belongs to

8553ad63fffffff.parquet

8553ad67fffffff.parquet

8553ad6ffffffff.parquet

world_countries_simple.parquet

[admin_code] ISO 3166-1 alpha-3 country code

[admin_code] ISO 3166-1 alpha-2 country code

geometry

[geometry] Country polygon boundary

Source Cooperative product page

providers: role [producer] for 'CNG' is an AI guess — Inferred as producer from the dataset title 'geocoding-cng'; no README or explicit attribution available.. Verify before flipping to _state: reviewed.

extent.spatial.bbox: omitted; spatial reference is categorical via [postcode_index.postcode (zipcode), postcode_index.region_ar (placename), postcode_index.region_en (placename), region_index.region_ar (placename), sa_districts_simple.geometry (geometry), sa_districts_simple.centroid (geometry), sa_districts_simple.city (placename), sa_governorates_simple.geometry (geometry), sa_governorates_simple.centroid (geometry), sa_regions_simple.geometry (geometry), sa_regions_simple.centroid (geometry), 8553ad1bfffffff.longitude (latlon), 8553ad1bfffffff.latitude (latlon), 8553ad1bfffffff.postcode (zipcode), 8553ad1bfffffff.district_ar (placename), 8553ad1bfffffff.city (placename), 8553ad1bfffffff.region_ar (placename), 8553ad1bfffffff.h3_index (spatial_index), world_countries_simple.geometry (geometry), world_countries_simple.iso_a3 (admin_code), world_countries_simple.iso_a2 (admin_code)]. Add bbox once a placename → bbox gazetteer is available.


Overture Maps - Places Dataset (GeoParquet) - Riyadh, Saudi Arabia

A collection of 28,637 places (areas of interest) across Riyadh, Saudi Arabia, derived from Overture Maps and combining remote-sensed data from Microsoft and Meta corporations, provided in cloud-native GeoParquet format.

Dataset files (1 x .parquet)

Source Cooperative product page

## README.md

# Overture Maps - Places Dataset - Riyadh, Saudi Arabia

![Riyadh AOIs](https://i.imgur.com/g4vucn8.png)

# Overview
This dataset includes Overture Maps Places Data which is derived from Microsoft and Meta Corporations.
It contains 28,637 Places (AOIs) Data for Riyadh Region, Saudi Arabia. Last Updated On 11 February 2024.
The dataset was derived from Overture Maps By https://tabaqat.net/ in GeoParquet Format which is one of Cloud Native Geospatial Formats.

# Observe it in use
You can use https://youssef-harby.github.io/OvertureMapsDownloader/ to get a quick overview to see it in action through shared URL provided here https://source.coop/tabaqat/riyadh-places/riyadh_places.parquet by changing or removing bbox and features limit parameters.

# Files and Structure
- `riyadh_places.parquet` - geoparquet file of Riyadh AOIs dataset.
- `README.md` - This file, explaining the repository.

# Data Sources:
  This dataset was compiled by Overture Maps using data from Meta and Microsoft corporations.

# Data Formats:
the data is provided in this format:
- GeoParquet: which is an encoding for how to store geospatial vector data (point, lines, polygons) in Apache Parquet, a popular columnar storage format for tabular data.

# Schema:
This dataset adheres to Overture Maps Schema of Places theme explained here https://docs.overturemaps.org/schema/concepts/by-theme/places/

# Data Upload Process
Data Upload Process Steps are explained in our blog post https://blogs.tabaqat.net/en/unlocking-spatial-insights-a-guide-to-uploading-geoparquet-files-onto-source-cooperative-platform-for-enhanced-data-collaboration/

# Contact
For further details about the dataset or the processing steps, you can contact us via https://platform.tabaqat.net/contact-us

[https://github.com/sarah88932/GEE-Data-Script-files/blob/main/Riyadh%20AOIs-1.png]: https://github.com/sarah88932/GEE-Data-Script-files/blob/main/Riyadh%20AOIs-1.png


RGB satellite imagery of Riyadh Governorate, Saudi Arabia, derived from Sentinel-2 L2A data captured between July 1–14, 2024. Distributed as a single PMTiles archive suitable for web-based tile visualization. Processed from the Element84 Earth Search STAC catalog using the Tabaqat satellite tool.

remote-sensing satellite-imagery sentinel-2 pmtiles

Dataset files (1 x .pmtiles)

Source Cooperative product page

## README.md

# Sentinel-2 L2A RGB Satellite Imagery (PMTiles) - Riyadh Governorate, Saudi Arabia
![Riyadh Sentinel](https://i.imgur.com/saiubwz.png)
# Overview
This dataset includes (PMTiles) RGB Of Sentinel-2 L2A Satellite Imagery of Riyadh Governorate, Saudi Arabia Captured from (1 to 14) July 2024 ,
derived from https://earth-search.aws.element84.com/v1 by Tabaqat satellite tool
which is one of Tabaqat GeoApps tools provided by https://tabaqat.net/.

# Observe it in use
You can use https://pmtiles.io/ to get a quick overview to see it in action through shared URL provided here
https://data.source.coop/tabaqat/riyadh-satellite-pmtiles/riyadh_governorate.pmtiles at specified zoom extent.

# Files and Structure
- `riyadh_governorate.pmtiles` - PMTiles file of Riyadh Governorate.
- `README.md` - This file, explaining the repository.

# Data Sources:
  This dataset was compiled by tabaqat satellite tool from https://earth-search.aws.element84.com/v1.

# Data Formats:
the data is provided in this format:
- PMTiles is a single-file archive format for tiled data, usually used for visualization. As an “archive format”, PMTiles is similar to a ZIP file:
it contains the contents of many individual files inside of one PMTiles file. you can explore more information about PMTiles, from here https://guide.cloudnativegeo.org/pmtiles/intro.html.

# Data Access:
Generating PMTiles From Sentinel Satellite Imagery Produced By Tabaqat Satellite Tool is explained in our blog post https://tabaqat-wagtail.tabaqat.net/en/resources/blogs/generating-pmtiles-from-sentinel-satellite-imagery-produced-by-tabaqat-satellite-tool/.

# Data Upload Process
Data Upload Process Steps are explained in our blog post https://blogs.tabaqat.net/en/unlocking-spatial-insights-a-guide-to-uploading-geoparquet-files-onto-source-cooperative-platform-for-enhanced-data-collaboration/ which is valid for any data format.

# Contact
For further details about the dataset or the processing steps, you can contact us via https://platform.tabaqat.net/contact-us.

providers: role [producer] for 'Element84 (Earth Search)' is an AI guess — Credited as the data source (earth-search.aws.element84.com/v1); assumed producer role as the host of the source Sentinel-2 L2A STAC catalog.. Verify before flipping to _state: reviewed.


Sentinel-2 L2A RGB Satellite Imagery (COG) - Riyadh Governorate, Saudi Arabia

This repository include (Cloud Opimized Geotiff) Sentinel-2 L2A Satellite Imagery of Riyadh Governorate, Saudi Arabia from (1 to 14) July 2024 , derived from https://earth-search.aws.element84.com/v1 by Tabaqat satellite tool which is one of Tabaqat GeoApps tools provided by https://tabaqat.net/.

# You can navigate to @tabaqat to explore our repositories.

#sentinel #satellite #imagery #cog #aws #remotesensing #saudiarabia #tabaqat


Road network dataset for the Riyadh region of Saudi Arabia, containing 423,735 road features derived from Overture Maps (which sources from OpenStreetMap). The data is provided in PMTiles format, a cloud-native single-file archive suitable for tiled map visualization.

roads openstreetmap administrative-boundaries pmtiles

Dataset files (1 x .pmtiles)

Source Cooperative product page

## README.md

# Overture Maps - Roads Dataset (PMTiles) - Riyadh, Saudi Arabia

![Riyadh AOIs](https://i.imgur.com/Y8eQTP1.png)

# Overview
This dataset includes Overture Maps Roads Data which is retrieved from Open Street Maps Corporation.
It contains 423,735 Roads Features for Riyadh Region, Saudi Arabia. Last Updated On 12 February 2024.
The dataset was derived from Overture Maps By https://tabaqat.net/ in PMTiles Format which is one of Cloud Native Geospatial Formats.

# Observe it in use
You can use https://pmtiles.io/ to get a quick overview to see it in action through shared URL provided here https://data.source.coop/tabaqat/riyadh-overture-pmtiles/riyadh_riyadh_roads.pmtiles at specified zoom extent.

# Files and Structure
- `riyadh_roads.pmtiles` - PMTiles File of Riyadh Roads Dataset.
- `README.md` - This file, explaining the repository.

# Data Sources:
  This dataset was compiled by Overture Maps using data from Open Street Maps corporation.

# Data Formats:
the data is provided in this format:
- PMTiles is a single-file archive format for tiled data, usually used for visualization. As an “archive format”, PMTiles is similar to a ZIP file:
it contains the contents of many individual files inside of one PMTiles file. you can explore more information about PMTiles, from here https://guide.cloudnativegeo.org/pmtiles/intro.html.

# Data Access:
Generating PMTiles From Vector Geospatial Data is explained in our blog post https://tabaqat-wagtail.tabaqat.net/en/resources/blogs/generating-pmtiles-from-vector-geospatial-data-using-tippecanoe-and-go-pmtiles-tools/.

# Data Upload Process
Data Upload Process Steps are explained in our blog post https://blogs.tabaqat.net/en/unlocking-spatial-insights-a-guide-to-uploading-geoparquet-files-onto-source-cooperative-platform-for-enhanced-data-collaboration/

# Schema:
This dataset adheres to Overture Maps Schema of Places theme explained here https://docs.overturemaps.org/schema/concepts/by-theme/transportation/roads/

# Contact
For further details about the dataset or the processing steps, you can contact us via https://platform.tabaqat.net/contact-us

providers: role [producer, licensor] for 'OpenStreetMap' is an AI guess — OSM is the ultimate data source; licensor role assumed based on OSM's ODbL governing derived datasets. Verify before flipping to _state: reviewed.

providers: role [processor] for 'Overture Maps Foundation' is an AI guess — Described as compiler of OSM data into the Overture Maps schema; processor role inferred. Verify before flipping to _state: reviewed.

providers: role [processor, host] for 'Tabaqat' is an AI guess — Derived the PMTiles file from Overture Maps data and uploaded to Source Cooperative; host role inferred from their platform presence. Verify before flipping to _state: reviewed.


Cloud 3D - GOES-16 Tropical Cyclones Finetuning Dataset

Cloud3DTACO is a large-scale AI-ready dataset pairing 2D multispectral geostationary imagery from GOES-16, Himawari-8/9, and MSG/SEVIRI with co-located 3D vertical cloud profiles from the CloudSat radar, comprising roughly 500,000 samples organized for self-supervised pretraining and supervised finetuning. This specific subset contains 121 GOES-16 finetuning samples focused on tropical cyclone structures (eyewalls and rainbands) derived from IBTrACS best-track records. Each sample consists of a geostationary imagery patch paired with an aligned CloudSat vertical profile label, both stored as Cloud-Optimized GeoTIFFs.

remote-sensing machine-learning satellite-imagery earth-observation meteorology radar tropical-cyclones cloud-microphysics

Dataset files (771242 x .tif)

Source Cooperative product page

## README.md

# A Global, AI-Ready Dataset for 3D Cloud Reconstruction

**Cesar Aybar**¹ · **Shirin Ermis**² · **Lilli Freischem**² · **Stella Girtsou**³⁴ · **Kyriaki-Margarita Bintsi**⁵
**Emiliano Diaz Salas-Porras**¹ · **Michael Eisinger**⁶ · **William Jones**² · **Anna Jungbluth**⁶ · **Benoit Tremblay**⁷

¹Universitat de València  ·  ²University of Oxford  ·  ³National Observatory of Athens  ·  ⁴National Technical University of Athens  ·  ⁵Harvard Medical School & Massachusetts General Hospital  ·  ⁶European Space Agency  ·  ⁷Environment and Climate Change Canada

---

## About

Cloud3DTACO pairs 2D multispectral geostationary imagery from **GOES-16**, **Himawari-8/9**, and **MSG/SEVIRI** with co-located 3D vertical cloud profiles from the **CloudSat** radar. The collection contains roughly 500,000 samples across 10 datasets, organized into a self-supervised **pretraining** stage (no labels) and a supervised **finetuning** stage (CloudSat profiles as ground truth). Each stage covers the three operational geostationary regions and includes a tropical-cyclone variant. The dataset is published as TACO v3 and follows FAIR principles.

## Datasets

| Subset | Pretraining (no labels) | Finetuning (CloudSat labels) |
|---|---|---|
| GOES-16 — Americas | [50,000 samples](https://source.coop/taco/3dclouds/pretraining/goes) | [31,046 samples](https://source.coop/taco/3dclouds/finetune/goes) |
| Himawari-8/9 — Asia & Oceania | [50,000 samples](https://source.coop/taco/3dclouds/pretraining/himawari) | [57,373 samples](https://source.coop/taco/3dclouds/finetune/himawari) |
| MSG/SEVIRI — Europe & Africa | [50,000 samples](https://source.coop/taco/3dclouds/pretraining/msg) | [181,653 samples](https://source.coop/taco/3dclouds/finetune/msg) |
| GOES — Tropical cyclones | [41,423 samples](https://source.coop/taco/3dclouds/pretraining/goes-cyclone) | [121 samples](https://source.coop/taco/3dclouds/finetune/goes-cyclone) |
| Himawari — Tropical cyclones | [38,711 samples](https://source.coop/taco/3dclouds/pretraining/himawari-cyclone) | [361 samples](https://source.coop/taco/3dclouds/finetune/himawari-cyclone) |
| **Total** | **230,134** | **270,554** |

## Storage

- **Pretraining** — each sample is a single multi-band Cloud-Optimized GeoTIFF at `DATA/{idx}.tif`.
- **Finetuning** — each sample is a folder with two COGs: `DATA/{idx}/geo_patch.tif` (geostationary imagery) and `DATA/{idx}/cloudsat_aligned.tif` (vertical cloud profile labels).

## Notebooks

- [**Demo 1 — Dataset Exploration & Visualization**](https://colab.research.google.com/drive/1ecZF5p_A_BkBi_G0LtizqeRgmNWFn2sq)
- [**Demo 2 — Self-Supervised Pretraining**](https://colab.research.google.com/drive/1z_xK7-Irp8Ohba_l3dtIGavRjQpxT3Zc)
- [**Demo 3 — Supervised Finetuning**](https://colab.research.google.com/drive/1P0CLVEGo-puhG2cHkpjc4BUooVm7nx4t)
- **Demo 4 — Pretraining + Finetuning** *(coming soon)*

## Acknowledgements

This work was enabled by the Frontier Development Lab (FDL) Earth Systems Lab, a public-private partnership between the European Space Agency (ESA), Trillium Technologies, and the University of Oxford. We thank the teams behind the GOES, Himawari, and Meteosat satellites for their invaluable data. This research was supported by computational resources from Google Cloud.


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