{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/xview-objects-in-context-in-overhead-imagery","title":"xView: Objects in Context in Overhead Imagery","arxiv_id":"1802.07856","date":"2018-02-22","proceeding":null,"authors":["Darius Lam","Richard Kuzma","Kevin McGee","Samuel Dooley","Michael Laielli","Matthew Klaric","Yaroslav Bulatov","Brendan McCord"],"abstract":"We introduce a new large-scale dataset for the advancement of object\ndetection techniques and overhead object detection research. This satellite\nimagery dataset enables research progress pertaining to four key computer\nvision frontiers. We utilize a novel process for geospatial category detection\nand bounding box annotation with three stages of quality control. Our data is\ncollected from WorldView-3 satellites at 0.3m ground sample distance, providing\nhigher resolution imagery than most public satellite imagery datasets. We\ncompare xView to other object detection datasets in both natural and overhead\nimagery domains and then provide a baseline analysis using the Single Shot\nMultiBox Detector. xView is one of the largest and most diverse publicly\navailable object-detection datasets to date, with over 1 million objects across\n60 classes in over 1,400 km^2 of imagery.","url_abs":"http://arxiv.org/abs/1802.07856v1","url_pdf":"http://arxiv.org/pdf/1802.07856v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"xview-objects-in-context-in-overhead-imagery","repo_url":"https://github.com/jshermeyer/RFSR","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"xview-objects-in-context-in-overhead-imagery","repo_url":"https://github.com/ultralytics/xview-yolov3","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"AGPL-3.0"}}],"tasks":[{"task_slug":"object","task_name":"Object"},{"task_slug":"object-detection","task_name":"Object Detection"},{"task_slug":"object-detection-in-aerial-images","task_name":"Object Detection In Aerial Images"},{"task_slug":"object-detection-1","task_name":"object-detection"}],"methods":[],"datasets_introduced":[{"slug":"xview","name":"xView","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1802.07856","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}