Geospatial data

ISERV

ISS SERVIR Environmental Research and Visualization System (ISERV) was a fully-automated prototype camera aboard the International Space Station that was tasked to capture high-resolution Earth imagery of specific locations at 3-7 frames per second. In the course of its regular operations during 2013 and 2014, ISERV's camera acquired images that can be used primaliry in use is environmental and disaster management.

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Data coverage

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The data is released under a ODC Public Domain Dedication & License 1.0 (PDDL-1.0).

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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