Disaster assessment using computer vision and satellite imagery: Applications in detecting water-related building damages

Cited 3 time in webofscience Cited 0 time in scopus
  • Hit : 312
  • Download : 0
The increasing frequency and severity of water-related disasters such as floods, tornadoes, hurricanes, and tsunamis in low- and middle-income countries exemplify the uneven effects of global climate change. The vulnerability of high-risk societies to natural disasters has continued to increase. To develop an effective and efficient adaptation strategy, local damage assessments must be timely, exhaustive, and accurate. We propose a novel deep-learning-based solution that uses pairs of pre- and post-disaster satellite images to identify water-related disaster-affected regions. The model extracts features of pre- and post-disaster images and uses the feature difference with them to predict damage in the pair. We demonstrate that the model can successfully identify local destruction using less granular and less complex ground-truth data than those used by previous segmentation models. When tested with various water-related disasters, our detection model reported an accuracy of 85.9% in spotting areas with damaged buildings. It also achieved a reliable performance of 80.3% in out-of-domain settings. Our deep learning-based damage assessment model can help direct resources to areas most vulnerable to climate disasters, reducing their impacts while promoting adaptive capacities for climate-resilient development in the most vulnerable regions.
Publisher
FRONTIERS MEDIA SA
Issue Date
2022-10
Language
English
Article Type
Article
Citation

FRONTIERS IN ENVIRONMENTAL SCIENCE, v.10, no.0, pp.1 - 14

ISSN
2296-665X
DOI
10.3389/fenvs.2022.969758
URI
http://hdl.handle.net/10203/299340
Appears in Collection
STP-Journal Papers(저널논문)MG-Journal Papers(저널논문)CS-Journal Papers(저널논문)
Files in This Item
There are no files associated with this item.
This item is cited by other documents in WoS
⊙ Detail Information in WoSⓡ Click to see webofscience_button
⊙ Cited 3 items in WoS Click to see citing articles in records_button

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0