{"url":"/dataset/gvlm","name":"GVLM","full_name":"Global Very-High-Resolution Landslide Mapping","description_markdown":"For change detection tasks, current open-source datasets mainly focus on building extraction (e.g., WHU building dataset and LEVIR-CD dataset) (Chen and Shi, 2020; Ji et al., 2018) and urban development monitoring (e.g., SECOND dataset, Google dataset and CDD dataset) (Yang et al., 2022; Peng et al., 2021; Lebedev et al., 2018), whereas datasets for natural disaster monitoring have been seldom investigated. \r\n\r\nTherefore, we sought to present the GVLM dataset, the first large-scale and open-source VHR landslide mapping dataset. It includes $17$ bitemporal very-high-resolution imagery pairs with a spatial resolution of $0.59$ m acquired via Google Earth service. Each sub-dataset contains a pair of bitemporal images and the corresponding ground-truth map. The total coverage of the dataset is $163.77 km2$. The landslide sites in different geographical locations have various sizes, shapes, occurrence times, spatial distributions, phenology states, and land cover types, resulting in considerable spectral heterogeneity and intensity variations in the remote sensing imagery. The GVLM dataset can be used to develop and evaluate machine/deep learning models for change detection, semantic segmentation and landslide extraction.","description_withheld":null,"homepage":"https://github.com/zxk688/GVLM","introduced_date":"2023-02-01","introduced_date_note":null,"introduced_by":null,"license":null,"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Change Detection","url":"/task/change-detection","datasets_with_task":"/datasets/task/change-detection"},{"name":"Semi-supervised Change Detection","url":"/task/semi-supervised-change-detection","datasets_with_task":"/datasets/task/semi-supervised-change-detection"},{"name":"Change detection for remote sensing images","url":"/task/change-detection-for-remote-sensing-images","datasets_with_task":"/datasets/task/change-detection-for-remote-sensing-images"},{"name":"Landslide segmentation","url":"/task/landslide-segmentation","datasets_with_task":"/datasets/task/landslide-segmentation"}],"languages":[],"variants":["GVLM"],"data_loaders":[{"repo":"https://github.com/zxk688/GVLM","url":"https://github.com/zxk688/GVLM","frameworks":["pytorch"]},{"repo":"https://github.com/zxk688/PG-DPRL","url":"https://github.com/zxk688/PG-DPRL","frameworks":["pytorch"]}],"num_papers_in_archive":4,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/change-detection-on-gvlm","task":"Change Detection","dataset_variant":"GVLM","rows":4,"metrics":["F1"],"first_row_in_archive_order":{"model":"BTC","paper":"/paper/be-the-change-you-want-to-see-revisiting","metrics":{"F1":"90.7"},"code_links":[{"title":"blaz-r/BTC-change-detection","url":"https://github.com/blaz-r/BTC-change-detection"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/be-the-change-you-want-to-see-revisiting","title":"Be the Change You Want to See: Revisiting Remote Sensing Change Detection Practices","date":"2025-07-04","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/mtp-advancing-remote-sensing-foundation-model","title":"MTP: Advancing Remote Sensing Foundation Model via Multi-Task Pretraining","date":"2024-03-20","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":5,"samples_ran":4,"samples_unverified":1,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/seasonal-contrast-unsupervised-pre-training","title":"Seasonal Contrast: Unsupervised Pre-Training from Uncurated Remote Sensing Data","date":"2021-03-30","rows_on_this_dataset":1,"code_links":5,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":8,"samples_ran":0,"samples_unverified":8,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/fully-convolutional-siamese-networks-for","title":"Fully Convolutional Siamese Networks for Change Detection","date":"2018-10-19","rows_on_this_dataset":1,"code_links":5,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":2,"samples_harvested":13,"samples_ran":4,"samples_unverified":9,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":1,"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."}