{"url":"/dataset/worldstrat","name":"WorldStrat","full_name":"The WorldStrat Dataset: Open High-Resolution Satellite Imagery With Paired Multi-Temporal Low-Resolution","description_markdown":"**Nearly 10,000 km² of free high-resolution and paired multi-temporal low-resolution satellite imagery** of unique locations which ensure stratified representation of all types of land-use across the world: from agriculture to ice caps, from forests to multiple urbanization densities. ​\r\n\r\nThose locations are also enriched with typically under-represented locations in ML datasets: sites of humanitarian interest, illegal mining sites, and settlements of persons at risk. \r\n​\r\nEach high-resolution image (Airbus SPOT at up to 1.5 m/pixel) comes with multiple temporally-matched low-resolution images from the freely accessible lower-resolution Sentinel-2 satellites (up to 10 m/pixel, 12 spectral bands).\r\n​\r\nThe dataset is accompanied with a paper, datasheet for datasets and an open-source Python package to: rebuild or extend the WorldStrat dataset, train and infer baseline algorithms, and learn with abundant tutorials, all compatible with the popular EO-learn toolbox. \r\n\r\nThe hope is to foster broad-spectrum applications of ML to satellite imagery, and possibly develop the same power of analysis allowed by costly private high-resolution imagery from free public low-resolution Sentinel2 imagery. We illustrate this specific point by training and releasing several highly compute-efficient baselines on the task of Multi-Frame Super-Resolution.","description_withheld":null,"homepage":"https://zenodo.org/record/6810792","introduced_date":"2022-07-13","introduced_date_note":null,"introduced_by":{"paper":"/paper/open-high-resolution-satellite-imagery-the","title":"Open High-Resolution Satellite Imagery: The WorldStrat Dataset -- With Application to Super-Resolution","first_author":"Julien Cornebise","url":null},"license":{"name":"CC BY  and CC BY-NC","url":"https://zenodo.org/record/6810792/files/LICENSE.txt"},"modalities":[{"name":"Images","url":"/datasets/modality/images"},{"name":"Time series","url":"/datasets/modality/time-series"}],"tasks":[{"name":"Satellite Image Classification","url":"/task/satellite-image-classification","datasets_with_task":"/datasets/task/satellite-image-classification"},{"name":"satellite image super-resolution","url":"/task/satellite-image-super-resolution","datasets_with_task":"/datasets/task/satellite-image-super-resolution"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["WorldStrat"],"data_loaders":[{"repo":"https://github.com/worldstrat/worldstrat","url":"https://github.com/worldstrat/worldstrat","frameworks":["pytorch"]}],"num_papers_in_archive":8,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}