{"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/functional-map-of-the-world","title":"Functional Map of the World","arxiv_id":"1711.07846","date":"2017-11-21","proceeding":"CVPR 2018 6","authors":["Gordon Christie","Neil Fendley","James Wilson","Ryan Mukherjee"],"abstract":"We present a new dataset, Functional Map of the World (fMoW), which aims to\ninspire the development of machine learning models capable of predicting the\nfunctional purpose of buildings and land use from temporal sequences of\nsatellite images and a rich set of metadata features. The metadata provided\nwith each image enables reasoning about location, time, sun angles, physical\nsizes, and other features when making predictions about objects in the image.\nOur dataset consists of over 1 million images from over 200 countries. For each\nimage, we provide at least one bounding box annotation containing one of 63\ncategories, including a \"false detection\" category. We present an analysis of\nthe dataset along with baseline approaches that reason about metadata and\ntemporal views. Our data, code, and pretrained models have been made publicly\navailable.","url_abs":"http://arxiv.org/abs/1711.07846v3","url_pdf":"http://arxiv.org/pdf/1711.07846v3.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":"functional-map-of-the-world","repo_url":"https://github.com/KellyYutongHe/satellite-pixel-synthesis-pytorch","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"functional-map-of-the-world","repo_url":"https://github.com/fMoW/dataset","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"NOASSERTION"}},{"paper_slug":"functional-map-of-the-world","repo_url":"https://github.com/facebookresearch/DomainBed","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"functional-map-of-the-world","repo_url":"https://github.com/fmow/baseline","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}},{"paper_slug":"functional-map-of-the-world","repo_url":"https://github.com/madrylab/datamodels-data","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"functional-map-of-the-world","repo_url":"https://github.com/mlaico/fmow-helper","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}},{"paper_slug":"functional-map-of-the-world","repo_url":"https://github.com/sustainlab-group/geography-aware-ssl","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"temporal-sequences","task_name":"Temporal Sequences"}],"methods":[],"datasets_introduced":[{"slug":"fmow","name":"fMoW","full_name":"Functional Map of the World"}],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1711.07846","atlas_url":"https://app.syntology.ai/?focus=1711.07846","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}