{"url":"/dataset/deepglobe","name":"DeepGlobe","full_name":null,"description_markdown":"We observe that satellite imagery is a powerful source of information as it contains more structured and uniform data, compared to traditional images. Although computer vision community has been accomplishing hard tasks on everyday image datasets using deep learning, satellite images are only recently gaining attention for maps and population analysis. This workshop aims at bringing together a diverse set of researchers to advance the state-of-the-art in satellite image analysis.\r\n\r\nTo direct more attention to such approaches, we propose DeepGlobe Satellite Image Understanding Challenge, structured around three different satellite image understanding tasks. The datasets created and released for this competition may serve as reference benchmarks for future research in satellite image analysis. Furthermore, since the challenge tasks will involve \"in the wild\" forms of classic computer vision problems, these datasets have the potential to become valuable testbeds for the design of robust vision algorithms, beyond the area of remote sensing.","description_withheld":null,"homepage":"http://deepglobe.org/","introduced_date":"2018-05-17","introduced_date_note":null,"introduced_by":{"paper":"/paper/deepglobe-2018-a-challenge-to-parse-the-earth","title":"DeepGlobe 2018: A Challenge to Parse the Earth through Satellite Images","first_author":"Ilke Demir","url":null},"license":null,"modalities":[],"tasks":[{"name":"Road Segmentation","url":"/task/road-segementation","datasets_with_task":"/datasets/task/road-segementation"},{"name":"Land Cover Classification","url":"/task/land-cover-classification","datasets_with_task":"/datasets/task/land-cover-classification"}],"languages":[],"variants":["DeepGlobe"],"data_loaders":[],"num_papers_in_archive":127,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/road-segementation-on-deepglobe","task":"Road Segmentation","dataset_variant":"DeepGlobe","rows":3,"metrics":["APLS","IoU","mIoU"],"first_row_in_archive_order":{"model":"SPIN Road Mapper (ours)","paper":"/paper/spin-road-mapper-extracting-roads-from-aerial","metrics":{"APLS":"0.7414","IoU":"0.6702"},"code_links":[{"title":"wgcban/spin_roadmapper","url":"https://github.com/wgcban/spin_roadmapper"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/threshnet-segmentation-refinement-inspired-by","title":"PatchRefineNet: Improving Binary Segmentation by Incorporating Signals from Optimal Patch-wise Binarization","date":"2022-11-12","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/spin-road-mapper-extracting-roads-from-aerial","title":"SPIN Road Mapper: Extracting Roads from Aerial Images via Spatial and Interaction Space Graph Reasoning for Autonomous Driving","date":"2021-09-16","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/d-linknet-linknet-with-pretrained-encoder-and","title":"D-LinkNet: LinkNet with Pretrained Encoder and Dilated Convolution for High Resolution Satellite Imagery Road Extraction","date":"2018-12-08","rows_on_this_dataset":1,"code_links":5,"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."}