{"url":"/dataset/urban-environments-dataset","name":"Urban Environments","full_name":null,"description_markdown":"The Urban Environments dataset is a dataset of 20 land use classes across 300 European cities paired with satellite imagery data.\r\n\r\nSource: [https://arxiv.org/abs/1704.02965](https://arxiv.org/abs/1704.02965)\r\nImage Source: [https://github.com/adrianalbert/urban-environments](https://github.com/adrianalbert/urban-environments)","description_withheld":null,"homepage":"https://github.com/adrianalbert/urban-environments","introduced_date":null,"introduced_date_note":null,"introduced_by":{"paper":"/paper/using-convolutional-networks-and-satellite","title":"Using convolutional networks and satellite imagery to identify patterns in urban environments at a large scale","first_author":"Adrian Albert","url":null},"license":{"name":"Unknown","url":null},"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Image Classification","url":"/task/image-classification","datasets_with_task":"/datasets/task/image-classification"}],"languages":[],"variants":["Urban Environments"],"data_loaders":[{"repo":"https://github.com/adrianalbert/urban-environments","url":"https://github.com/adrianalbert/urban-environments","frameworks":["tf"]}],"num_papers_in_archive":6,"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."}