multi temporal crop classification

TerraMesh A planetary‑scale, multimodal analysis‑ready dataset for Earth‑Observation foundation models: TerraMesh merges data from Sentinel‑1 SAR, Sentinel‑2 optical, Copernicus DEM, NDVI, and land‑cover sources into more than 9 million co‑registered patches ready for large‑scale representation learning. You find more information about the data sampling and preprocessing in our paper: TerraMesh: A Planetary Mosaic of Multimodal Earth Observation Data. Samples from the TerraMesh… See the full description on the dataset page: https://huggingface.co/datasets/ibm-esa-geospatial/TerraMesh.

Rasters/CONUS_NLCD_10yr_intervals

Time series of annual global maps of land use and land cover (LULC). It currently has data from 2017-2023. The maps are derived from ESA Sentinel-2 imagery at 10m resolution. 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, 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 - 2023. 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 collections from the Mircosoft Planetary Computer. Each of the maps has an assessed average accuracy of over 75%. These maps have been improved from Impact Observatory’s previous release and provide a relative reduction in the amount of anomalous change between classes, particularly between “Bare” and any of the vegetative classes “Trees,” “Crops,” “Flooded Vegetation,” and “Rangeland”. This updated time series of annual global maps is also re-aligned to match the ESA UTM tiling grid for Sentinel-2 imagery. All years are available under a Creative Commons BY-4.0.

Remote Sensing Change Detection Dataset For Chinese documentation, please see README_zh.md Dataset Description A specialized dataset for remote sensing change detection research, containing complete image processing pipeline and annotation information. This dataset includes 24 groups of registered and aligned remote sensing image samples, with each group containing 5 different types of image files and corresponding annotation files. Dataset Features… See the full description on the dataset page: https://huggingface.co/datasets/Mercyiris/remote-sensing-change-detection.

umbra_test_noncog

CONUS H3 Land Cover Density (NLCD 2024) A continental United States land cover dataset aggregated to Uber's H3 hexagonal grid at resolution 10 (~120m cell edge length). Each cell contains continuous per-channel coverage fractions derived from the USGS National Land Cover Database (NLCD) 2024. Unlike NLCD's native discrete classification, this dataset expresses land cover as continuous multi-channel density values — a cell at a forest-wetland boundary is represented as 0.6 forest… See the full description on the dataset page: https://huggingface.co/datasets/Timmahw/conus-h3-land-cover.