Zobrazeno 1 - 10
of 1 339
pro vyhledávání: '"Building damage detection"'
Akademický článek
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Publikováno v:
International Journal of Digital Earth, Vol 17, Iss 1 (2024)
ABSTRACTRapidly estimating post-disaster building damage via high-resolution remote sensing (HRRS) imagery is essential for initial disaster relief. However, the complex appearance of building damage poses challenges for existing methods. Specificall
Externí odkaz:
https://doaj.org/article/d0e5a7e492604600ae4a9926b69d2da5
We construct a strong baseline method for building damage detection by starting with the highly-engineered winning solution of the xView2 competition, and gradually stripping away components. This way, we obtain a much simpler method, while retaining
Externí odkaz:
http://arxiv.org/abs/2401.17271
Publikováno v:
Journal of Intelligent & Fuzzy Systems. 2024, Vol. 46 Issue 1, p389-404. 16p.
Existing Building Damage Detection (BDD) methods always require labour-intensive pixel-level annotations of buildings and their conditions, hence largely limiting their applications. In this paper, we investigate a challenging yet practical scenario
Externí odkaz:
http://arxiv.org/abs/2312.01576
Autor:
Wang, Chenguang, Liu, Yepeng, Zhang, Xiaojian, Li, Xuechun, Paramygin, Vladimir, Subgranon, Arthriya, Sheng, Peter, Zhao, Xilei, Xu, Susu
Timely and accurate assessment of hurricane-induced building damage is crucial for effective post-hurricane response and recovery efforts. Recently, remote sensing technologies provide large-scale optical or Interferometric Synthetic Aperture Radar (
Externí odkaz:
http://arxiv.org/abs/2310.01565
Akademický článek
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Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 13120-13134 (2024)
Natural disasters commonly occur in all regions around the world and cause huge financial and human losses. One of the main effects of earthquakes and floods is the destruction of buildings. Photogrammetric and remote sensing (RS) data track changes
Externí odkaz:
https://doaj.org/article/30f47286353442d491108d40ac6b2ba0
Publikováno v:
International Journal of Digital Earth, Vol 17, Iss 1 (2024)
ABSTRACTDeep learning has been extensively utilized in the assessment of building damage after disasters. However, the field of building damage segmentation faces challenges, such as misjudged regions, high network complexity, and long running times.
Externí odkaz:
https://doaj.org/article/5c792ecb4737435a9ff5073c07994504
Autor:
Wang, Chenguang, Liu, Yepeng, Zhang, Xiaojian, Li, Xuechun, Paramygin, Vladimir, Sheng, Peter, Zhao, Xilei, Xu, Susu
Publikováno v:
In International Journal of Disaster Risk Reduction 1 April 2024 104