{"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/slum-segmentation-and-change-detection-a-deep","title":"Slum Segmentation and Change Detection : A Deep Learning Approach","arxiv_id":"1811.07896","date":"2018-11-19","proceeding":null,"authors":["Shishira R Maiya","Sudharshan Chandra Babu"],"abstract":"More than one billion people live in slums around the world. In some\ndeveloping countries, slum residents make up for more than half of the\npopulation and lack reliable sanitation services, clean water, electricity,\nother basic services. Thus, slum rehabilitation and improvement is an important\nglobal challenge, and a significant amount of effort and resources have been\nput into this endeavor. These initiatives rely heavily on slum mapping and\nmonitoring, and it is essential to have robust and efficient methods for\nmapping and monitoring existing slum settlements. In this work, we introduce an\napproach to segment and map individual slums from satellite imagery, leveraging\nregional convolutional neural networks for instance segmentation using transfer\nlearning. In addition, we also introduce a method to perform change detection\nand monitor slum change over time. We show that our approach effectively learns\nslum shape and appearance, and demonstrates strong quantitative results,\nresulting in a maximum AP of 80.0.","url_abs":"http://arxiv.org/abs/1811.07896v1","url_pdf":"http://arxiv.org/pdf/1811.07896v1.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":"slum-segmentation-and-change-detection-a-deep","repo_url":"https://github.com/cbsudux/Mumbai-slum-segmentation","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"slum-segmentation-and-change-detection-a-deep","repo_url":"https://github.com/puneethshankar/slum-segmentation","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}}],"tasks":[{"task_slug":"change-detection","task_name":"Change Detection"},{"task_slug":"deep-learning","task_name":"Deep Learning"},{"task_slug":"instance-segmentation","task_name":"Instance Segmentation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"},{"task_slug":"transfer-learning","task_name":"Transfer Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1811.07896","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}