Building damage assessment for rapid disaster response with a deep object-based semantic change detection framework: From natural disasters to man-made disasters

•Deep object-based semantic change detection framework (ChangeOS) is proposed.•ChangeOS seamlessly integrates object-based image analysis and deep learning.•City-scale building damage assessment can be achieved within one minute.•A global-scale dataset is used to evaluate the effectiveness of Change...

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Veröffentlicht in:	Remote sensing of environment Jg. 265; S. 112636
Hauptverfasser:	Zheng, Zhuo, Zhong, Yanfei, Wang, Junjue, Ma, Ailong, Zhang, Liangpei
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Elsevier Inc 01.11.2021 Elsevier BV
Schlagworte:	Artificial neural networks Barracks Building damage Building damage assessment Change detection Classification Consistency Damage assessment Damage localization data collection Datasets Deep learning development aid Disaster management Disaster response Disasters environment humans Image analysis Image processing Image segmentation Information processing Localization Machine learning Man made disasters Natural disasters Neural networks OBIA Pattern recognition Post-production processing Remote sensing Representations Residential military buildings Semantics Spatial discrimination Spatial distribution Spatial resolution Statistical analysis statistics Deep learning Building damage assessment Disaster response Change detection Remote sensing OBIA
ISSN:	0034-4257, 1879-0704
Online-Zugang:	Volltext
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