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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| Vydáno v: | Remote sensing of environment Ročník 265; s. 112636 |
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| Hlavní autoři: | , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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New York
Elsevier Inc
01.11.2021
Elsevier BV |
| Témata: | |
| ISSN: | 0034-4257, 1879-0704 |
| On-line přístup: | Získat plný text |
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| Abstract | •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 ChangeOS.•Two local-scale datasets are used to show its great generalization ability.
Sudden-onset natural and man-made disasters represent a threat to the safety of human life and property. Rapid and accurate building damage assessment using bitemporal high spatial resolution (HSR) remote sensing images can quickly and safely provide us with spatial distribution information and statistics of the damage degree to assist with humanitarian assistance and disaster response. For building damage assessment, strong feature representation and semantic consistency are the keys to obtaining a high accuracy. However, the conventional object-based image analysis (OBIA) framework using a patch-based convolutional neural network (CNN) can guarantee semantic consistency, but with weak feature representation, while the Siamese fully convolutional network approach has strong feature representation capabilities but is semantically inconsistent. In this paper, we propose a deep object-based semantic change detection framework, called ChangeOS, for building damage assessment. To seamlessly integrate OBIA and deep learning, we adopt a deep object localization network to generate accurate building objects, in place of the superpixel segmentation commonly used in the conventional OBIA framework. Furthermore, the deep object localization network and deep damage classification network are integrated into a unified semantic change detection network for end-to-end building damage assessment. This also provides deep object features that can supply an object prior to the deep damage classification network for more consistent semantic feature representation. Object-based post-processing is adopted to further guarantee the semantic consistency of each object. The experimental results obtained on a global scale dataset including 19 natural disaster events and two local scale datasets including the Beirut port explosion event and the Bata military barracks explosion event show that ChangeOS is superior to the currently published methods in speed and accuracy, and has a superior generalization ability for man-made disasters. |
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| AbstractList | Sudden-onset natural and man-made disasters represent a threat to the safety of human life and property. Rapid and accurate building damage assessment using bitemporal high spatial resolution (HSR) remote sensing images can quickly and safely provide us with spatial distribution information and statistics of the damage degree to assist with humanitarian assistance and disaster response. For building damage assessment, strong feature representation and semantic consistency are the keys to obtaining a high accuracy. However, the conventional object-based image analysis (OBIA) framework using a patch-based convolutional neural network (CNN) can guarantee semantic consistency, but with weak feature representation, while the Siamese fully convolutional network approach has strong feature representation capabilities but is semantically inconsistent. In this paper, we propose a deep object-based semantic change detection framework, called ChangeOS, for building damage assessment. To seamlessly integrate OBIA and deep learning, we adopt a deep object localization network to generate accurate building objects, in place of the superpixel segmentation commonly used in the conventional OBIA framework. Furthermore, the deep object localization network and deep damage classification network are integrated into a unified semantic change detection network for end-to-end building damage assessment. This also provides deep object features that can supply an object prior to the deep damage classification network for more consistent semantic feature representation. Object-based post-processing is adopted to further guarantee the semantic consistency of each object. The experimental results obtained on a global scale dataset including 19 natural disaster events and two local scale datasets including the Beirut port explosion event and the Bata military barracks explosion event show that ChangeOS is superior to the currently published methods in speed and accuracy, and has a superior generalization ability for 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 ChangeOS.•Two local-scale datasets are used to show its great generalization ability. Sudden-onset natural and man-made disasters represent a threat to the safety of human life and property. Rapid and accurate building damage assessment using bitemporal high spatial resolution (HSR) remote sensing images can quickly and safely provide us with spatial distribution information and statistics of the damage degree to assist with humanitarian assistance and disaster response. For building damage assessment, strong feature representation and semantic consistency are the keys to obtaining a high accuracy. However, the conventional object-based image analysis (OBIA) framework using a patch-based convolutional neural network (CNN) can guarantee semantic consistency, but with weak feature representation, while the Siamese fully convolutional network approach has strong feature representation capabilities but is semantically inconsistent. In this paper, we propose a deep object-based semantic change detection framework, called ChangeOS, for building damage assessment. To seamlessly integrate OBIA and deep learning, we adopt a deep object localization network to generate accurate building objects, in place of the superpixel segmentation commonly used in the conventional OBIA framework. Furthermore, the deep object localization network and deep damage classification network are integrated into a unified semantic change detection network for end-to-end building damage assessment. This also provides deep object features that can supply an object prior to the deep damage classification network for more consistent semantic feature representation. Object-based post-processing is adopted to further guarantee the semantic consistency of each object. The experimental results obtained on a global scale dataset including 19 natural disaster events and two local scale datasets including the Beirut port explosion event and the Bata military barracks explosion event show that ChangeOS is superior to the currently published methods in speed and accuracy, and has a superior generalization ability for man-made disasters. |
| ArticleNumber | 112636 |
| Author | Ma, Ailong Zhang, Liangpei Wang, Junjue Zhong, Yanfei Zheng, Zhuo |
| Author_xml | – sequence: 1 givenname: Zhuo orcidid: 0000-0003-1811-6725 surname: Zheng fullname: Zheng, Zhuo email: zhengzhuo@whu.edu.cn organization: State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan 430074, China – sequence: 2 givenname: Yanfei orcidid: 0000-0001-9446-5850 surname: Zhong fullname: Zhong, Yanfei email: zhongyanfei@whu.edu.cn organization: State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan 430074, China – sequence: 3 givenname: Junjue orcidid: 0000-0002-9500-3399 surname: Wang fullname: Wang, Junjue organization: State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan 430074, China – sequence: 4 givenname: Ailong surname: Ma fullname: Ma, Ailong organization: State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan 430074, China – sequence: 5 givenname: Liangpei orcidid: 0000-0001-6890-3650 surname: Zhang fullname: Zhang, Liangpei organization: State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan 430074, China |
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| ContentType | Journal Article |
| Copyright | 2021 Elsevier Inc. Copyright Elsevier BV Nov 2021 |
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| Snippet | •Deep object-based semantic change detection framework (ChangeOS) is proposed.•ChangeOS seamlessly integrates object-based image analysis and deep... Sudden-onset natural and man-made disasters represent a threat to the safety of human life and property. Rapid and accurate building damage assessment using... |
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| SubjectTerms | 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 |
| Title | Building damage assessment for rapid disaster response with a deep object-based semantic change detection framework: From natural disasters to man-made disasters |
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