Geospatial data

GeoNadir FAIR Data

GeoNadir FAIR Data is an open collection of uncrewed aerial vehicle (UAV or drone) mapping datasets contributed through the GeoNadir platform and published as cloud-native geospatial files on Amazon S3. Each dataset represents one drone survey. Every dataset provides a normalized metadata file and a ZIP package of the original raw images. Where available, a dataset may also include an RGB orthomosaic, a multispectral orthomosaic, a Digital Surface Model (DSM), and a Digital Terrain Model (DTM), each stored as a Cloud Optimized GeoTIFF (COG) that can be read directly by GDAL, Rasterio, QGIS, and other cloud-native geospatial...

Listed for discovery. Access is provided by the original publisher under its terms.

Data coverage

GeoNadir FAIR Data is an open collection of uncrewed aerial vehicle (UAV or drone) mapping datasets contributed through the GeoNadir platform and published as cloud-native geospatial files on Amazon S3. Each dataset represents one drone survey. Every dataset provides a normalized metadata file and a ZIP package of the original raw images. Where available, a dataset may also include an RGB orthomosaic, a multispectral orthomosaic, a Digital Surface Model (DSM), and a Digital Terrain Model (DTM), each stored as a Cloud Optimized GeoTIFF (COG) that can be read directly by GDAL, Rasterio, QGIS, and other cloud-native geospatial tools without downloading the full file. The files are addressable directly on Amazon S3 by a stable key convention, so the collection is fully usable today with standard tools such as boto3, Rasterio, GDAL, and QGIS: list the datasets prefix, read each survey's metadata file, and open its COGs by their known keys. A static STAC 1.1 catalog describes every dataset as a STAC Item and can be browsed with STAC Browser or read with pystac and other STAC clients. A STAC API for server-side search (by capture date, area of interest, and metadata fields such as IUCN habitat classification) is planned. The collection includes only datasets that GeoNadir is able to publish publicly under the Creative Commons Attribution 4.0 International license (CC-BY-4.0) license. Private workspace data and unpublished datasets are out of scope for this collection.

Access & permitted use

Review the publisher’s documentation for availability, eligibility and permitted uses, including commercial analysis, model training and redistribution.

This dataset is licensed under the Creative Commons Attribution 4.0 International license (CC-BY-4.0). You are free to share and adapt the material for any purpose, provided you give appropriate credit to GeoNadir and the contributing data provider. Full license text is available at https://creativecommons.org/licenses/by/4.0/

Need help sourcing data?

Tell us your required fields, population, geography and time period. We can review suitable sources and licensing options.

Request sourcing help

Related data sources

Climate and weatherPublic download

NOAA Global Daily Climate Observations

Daily precipitation, temperature, snowfall and other surface observations from the Global Historical Climatology Network across international stations.

NOAA National Centers for Environmental Information
Climate & weatherExternal source; review access terms

(EXPERIMENTAL) NOAA FourCastNet Global Forecast System (FourCastNetGFS) (EXPERIMENTAL)

The FourCastNet Global Forecast System (FourCastNetGFS) is an experimental system set up by the National Centers for Environmental Prediction (NCEP) to produce medium range global forecasts. The model runs on a 0.25 degree latitude-longitude grid (about 28 km) and 13 pressure levels. The model produces forecasts 4 times a day at 00Z, 06Z, 12Z and 18Z cycles. Major atmospheric and surface fields including temperature, wind components, geopotential height, relative humidity and 2 meter temperature and 10 meter winds are available. The products are 6 hourly forecasts up to 10 days. The data format is GRIB2. The FourCastNetGFS...

NOAA
Geospatial dataExternal source; review access terms

10m Annual Land Use Land Cover (9-class)

This dataset, produced by Impact Observatory, Microsoft, and Esri, displays a global map of land use and land cover (LULC) derived from ESA Sentinel-2 imagery at 10 meter resolution for the years 2017 - 2024. Each map is a composite of LULC predictions for 9 classes throughout the year in order to generate a representative snapshot of each year. This dataset was generated by Impact Observatory, which used billions of human-labeled pixels (curated by the National Geographic Society) to train a deep learning model for land classification. Each global map was produced by applying this model to the Sentinel-2 annual scene...

Impact Observatory