{"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/the-multi-temporal-urban-development-spacenet","title":"The Multi-Temporal Urban Development SpaceNet Dataset","arxiv_id":"2102.04420","date":"2021-02-08","proceeding":"CVPR 2021 1","authors":["Adam Van Etten","Daniel Hogan","Jesus Martinez-Manso","Jacob Shermeyer","Nicholas Weir","Ryan Lewis"],"abstract":"Satellite imagery analytics have numerous human development and disaster response applications, particularly when time series methods are involved. For example, quantifying population statistics is fundamental to 67 of the 231 United Nations Sustainable Development Goals Indicators, but the World Bank estimates that over 100 countries currently lack effective Civil Registration systems. To help address this deficit and develop novel computer vision methods for time series data, we present the Multi-Temporal Urban Development SpaceNet (MUDS, also known as SpaceNet 7) dataset. This open source dataset consists of medium resolution (4.0m) satellite imagery mosaics, which includes 24 images (one per month) covering >100 unique geographies, and comprises >40,000 km2 of imagery and exhaustive polygon labels of building footprints therein, totaling over 11M individual annotations. Each building is assigned a unique identifier (i.e. address), which permits tracking of individual objects over time. Label fidelity exceeds image resolution; this \"omniscient labeling\" is a unique feature of the dataset, and enables surprisingly precise algorithmic models to be crafted. We demonstrate methods to track building footprint construction (or demolition) over time, thereby directly assessing urbanization. Performance is measured with the newly developed SpaceNet Change and Object Tracking (SCOT) metric, which quantifies both object tracking as well as change detection. We demonstrate that despite the moderate resolution of the data, we are able to track individual building identifiers over time. This task has broad implications for disaster preparedness, the environment, infrastructure development, and epidemic prevention.","url_abs":"https://arxiv.org/abs/2102.04420v1","url_pdf":"https://arxiv.org/pdf/2102.04420v1.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":"the-multi-temporal-urban-development-spacenet","repo_url":"https://github.com/avanetten/avanetten.github.io","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"change-detection","task_name":"Change Detection"},{"task_slug":"disaster-response","task_name":"Disaster Response"},{"task_slug":"object-tracking","task_name":"Object Tracking"},{"task_slug":"time-series-1","task_name":"Time Series"},{"task_slug":"time-series","task_name":"Time Series Analysis"}],"methods":[],"datasets_introduced":[{"slug":"spacenet-7","name":"SpaceNet 7","full_name":"Multi-Temporal Urban Development SpaceNet Dataset"}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2102.04420","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}