{"url":"/dataset/rwanda-built-up-region-segmentation","name":"RWanda Built-up Region Segmentation","full_name":null,"description_markdown":"We create Rwanda built-up regions dataset, a different and versatile in nature from previously available datasets. The varying structure size and formation, irregular patterns of construction, buildings in forests and deserts, and the existence of mud houses make it very challenging. A total of 787 satellite images of size 256 × 256 are collected at a high resolution (HR) of 1.193 meters per pixel and hand tagged for built-up region segmentation using an online tool Label-Box.","description_withheld":null,"homepage":"http://im.itu.edu.pk/wan/","introduced_date":"2020-07-05","introduced_date_note":null,"introduced_by":{"paper":"/paper/weakly-supervised-domain-adaptation-for-built","title":"Weakly Supervised Domain Adaptation for Built-up Region Segmentation in Aerial and Satellite Imagery","first_author":"Javed Iqbal","url":null},"license":null,"modalities":[],"tasks":[{"name":"Domain Adaptation","url":"/task/domain-adaptation","datasets_with_task":"/datasets/task/domain-adaptation"},{"name":"2D Semantic Segmentation","url":"/task/2d-semantic-segmentation","datasets_with_task":"/datasets/task/2d-semantic-segmentation"}],"languages":[],"variants":["RWanda Built-up Region Segmentation"],"data_loaders":[],"num_papers_in_archive":1,"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."}