{"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/detecting-damaged-buildings-on-post-hurricane","title":"Building Damage Annotation on Post-Hurricane Satellite Imagery Based on Convolutional Neural Networks","arxiv_id":"1807.01688","date":"2018-07-04","proceeding":null,"authors":["Quoc Dung Cao","Youngjun Choe"],"abstract":"After a hurricane, damage assessment is critical to emergency managers for efficient response and resource allocation. One way to gauge the damage extent is to quantify the number of flooded/damaged buildings, which is traditionally done by ground survey. This process can be labor-intensive and time-consuming. In this paper, we propose to improve the efficiency of building damage assessment by applying image classification algorithms to post-hurricane satellite imagery. At the known building coordinates (available from public data), we extract square-sized images from the satellite imagery to create training, validation, and test datasets. Each square-sized image contains a building to be classified as either 'Flooded/Damaged' (labeled by volunteers in a crowd-sourcing project) or 'Undamaged'. We design and train a convolutional neural network from scratch and compare it with an existing neural network used widely for common object classification. We demonstrate the promise of our damage annotation model (over 97% accuracy) in the case study of building damage assessment in the Greater Houston area affected by 2017 Hurricane Harvey.","url_abs":"https://arxiv.org/abs/1807.01688v4","url_pdf":"https://arxiv.org/pdf/1807.01688v4.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":"detecting-damaged-buildings-on-post-hurricane","repo_url":"https://github.com/qcao10/DamageDetection","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null},{"paper_slug":"detecting-damaged-buildings-on-post-hurricane","repo_url":"https://github.com/ashwanirajan/Hurricane-Damage-Detection","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"detecting-damaged-buildings-on-post-hurricane","repo_url":"https://github.com/baky0905/hurricane-data-engineering","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"detecting-damaged-buildings-on-post-hurricane","repo_url":"https://github.com/johnson-shuffle/tdi-proposal","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"detecting-damaged-buildings-on-post-hurricane","repo_url":"https://github.com/natgable/cs-229-project","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"detecting-damaged-buildings-on-post-hurricane","repo_url":"https://github.com/team-data-science/azure-data-engineering","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"building-damage-assessment","task_name":"Building Damage Assessment"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"image-classification","task_name":"Image Classification"},{"task_slug":"image-classification","task_name":"image-classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}