{"url":"/dataset/so2sat-lcz42","name":"So2Sat LCZ42","full_name":null,"description_markdown":"So2Sat LCZ42 consists of local climate zone (LCZ) labels of about half a million Sentinel-1 and Sentinel-2 image patches in 42 urban agglomerations (plus 10 additional smaller areas) across the globe. This dataset was labeled by 15 domain experts following a carefully designed labeling work flow and evaluation process over a period of six months. \r\n\r\nSource: [So2Sat LCZ42: A Benchmark Dataset for Global Local Climate Zones Classification](/paper/so2sat-lcz42-a-benchmark-dataset-for-global)\r\nImage Source: [Zhu et al](https://paperswithcode.com/paper/so2sat-lcz42-a-benchmark-dataset-for-global)","description_withheld":null,"homepage":"https://mediatum.ub.tum.de/1483140","introduced_date":null,"introduced_date_note":null,"introduced_by":{"paper":"/paper/so2sat-lcz42-a-benchmark-dataset-for-global","title":"So2Sat LCZ42: A Benchmark Dataset for Global Local Climate Zones Classification","first_author":"Xiao Xiang Zhu","url":null},"license":null,"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Image Classification","url":"/task/image-classification","datasets_with_task":"/datasets/task/image-classification"},{"name":"Semantic Segmentation","url":"/task/semantic-segmentation","datasets_with_task":"/datasets/task/semantic-segmentation"},{"name":"Domain Adaptation","url":"/task/domain-adaptation","datasets_with_task":"/datasets/task/domain-adaptation"}],"languages":[],"variants":["So2Sat LCZ42"],"data_loaders":[{"repo":"https://github.com/tensorflow/datasets","url":"https://www.tensorflow.org/datasets/catalog/so2sat","frameworks":["tf","jax"]}],"num_papers_in_archive":11,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/image-classification-on-so2sat-lcz42","task":"Image Classification","dataset_variant":"So2Sat LCZ42","rows":1,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"ResNet50","paper":"/paper/in-domain-representation-learning-for-remote-1","metrics":{"Accuracy":"63.25"},"code_links":[{"title":"google-research/google-research","url":"https://github.com/google-research/google-research/tree/master/remote_sensing_representations"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/in-domain-representation-learning-for-remote-1","title":"In-domain representation learning for remote sensing","date":"2019-11-15","rows_on_this_dataset":1,"code_links":1,"syntology":null}],"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."}