all AI news
Post-hurricane building damage assessment using street-view imagery and structured data: A multi-modal deep learning approach
April 12, 2024, 4:45 a.m. | Zhuoqun Xue, Xiaojian Zhang, David O. Prevatt, Jennifer Bridge, Susu Xu, Xilei Zhao
cs.CV updates on arXiv.org arxiv.org
Abstract: Accurately assessing building damage is critical for disaster response and recovery. However, many existing models for detecting building damage have poor prediction accuracy due to their limited capabilities of identifying detailed, comprehensive structural and/or non-structural damage from the street-view image. Additionally, these models mainly rely on the imagery data for damage classification, failing to account for other critical information, such as wind speed, building characteristics, evacuation zones, and distance of the building to the hurricane …
abstract accuracy arxiv assessment building capabilities cs.cv data deep learning disaster disaster response however hurricane image modal multi-modal prediction recovery street structured data type view
More from arxiv.org / cs.CV updates on arXiv.org
Jobs in AI, ML, Big Data
AI Research Scientist
@ Vara | Berlin, Germany and Remote
Data Architect
@ University of Texas at Austin | Austin, TX
Data ETL Engineer
@ University of Texas at Austin | Austin, TX
Lead GNSS Data Scientist
@ Lurra Systems | Melbourne
Senior Machine Learning Engineer (MLOps)
@ Promaton | Remote, Europe
Senior Software Engineer, Generative AI (C++)
@ SoundHound Inc. | Toronto, Canada