{"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/from-satellite-imagery-to-disaster-insights","title":"From Satellite Imagery to Disaster Insights","arxiv_id":"1812.07033","date":"2018-12-17","proceeding":null,"authors":["Jigar Doshi","Saikat Basu","Guan Pang"],"abstract":"The use of satellite imagery has become increasingly popular for disaster\nmonitoring and response. After a disaster, it is important to prioritize rescue\noperations, disaster response and coordinate relief efforts. These have to be\ncarried out in a fast and efficient manner since resources are often limited in\ndisaster-affected areas and it's extremely important to identify the areas of\nmaximum damage. However, most of the existing disaster mapping efforts are\nmanual which is time-consuming and often leads to erroneous results. In order\nto address these issues, we propose a framework for change detection using\nConvolutional Neural Networks (CNN) on satellite images which can then be\nthresholded and clustered together into grids to find areas which have been\nmost severely affected by a disaster. We also present a novel metric called\nDisaster Impact Index (DII) and use it to quantify the impact of two natural\ndisasters - the Hurricane Harvey flood and the Santa Rosa fire. Our framework\nachieves a top F1 score of 81.2% on the gridded flood dataset and 83.5% on the\ngridded fire dataset.","url_abs":"http://arxiv.org/abs/1812.07033v1","url_pdf":"http://arxiv.org/pdf/1812.07033v1.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":"from-satellite-imagery-to-disaster-insights","repo_url":"https://github.com/GogulaK/Disaster-Impact-Prediction-using-Aerial-Satellite-Imagery","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":"disaster-response","task_name":"Disaster Response"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1812.07033","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}