Building damage assessment in natural disasters: A trans- and interdisciplinary approach combining domain knowledge, 3D machine learning, and crowdsourcing
Recent natural disasters have claimed many lives. Reliable damage predictions and timely assessments are essential for effective rescue operation planning and efficient allocation of limited resources. Currently, experts in the field perform damage assessment manually, which is resource- and time-in...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-04-01
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| Series: | Progress in Disaster Science |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590061725000249 |
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| author | Julia Kohns Vivien Zahs Carolin Klonner Bernhard Höfle Lothar Stempniewski Alexander Stark |
| author_facet | Julia Kohns Vivien Zahs Carolin Klonner Bernhard Höfle Lothar Stempniewski Alexander Stark |
| author_sort | Julia Kohns |
| collection | DOAJ |
| description | Recent natural disasters have claimed many lives. Reliable damage predictions and timely assessments are essential for effective rescue operation planning and efficient allocation of limited resources. Currently, experts in the field perform damage assessment manually, which is resource- and time-intensive. To address this issue, we propose a general trans- and interdisciplinary concept that combines the strengths of domain knowledge, automated computational methods, and crowdsourcing. The objective is to provide relevant and timely damage information after a natural disaster. The specific implementation presented for the earthquake damage use case includes (1) the development of a set of novel, innovative methods, (2) their combination to obtain timely and reliable damage information, (3) fully defined interfaces between all components to ensure an automated data flow, (4) implementation as a fully open-source framework, and (5) the participation of end users in the development of the framework from the beginning, contributing their expertise. Compared to other existing individual solutions, our interdisciplinary implementation has shown to provide fast and accurate information in disaster situations, aiding the management of consequences and saving lives. We consider the implementation transferable to various types of natural hazards due to its open-source realisation and the flexibility of its modules and interfaces. |
| format | Article |
| id | doaj-art-3f0e840aecdd4368b1a4ac4b51c6075d |
| institution | Kabale University |
| issn | 2590-0617 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Progress in Disaster Science |
| spelling | doaj-art-3f0e840aecdd4368b1a4ac4b51c6075d2025-08-20T03:47:02ZengElsevierProgress in Disaster Science2590-06172025-04-012610042710.1016/j.pdisas.2025.100427Building damage assessment in natural disasters: A trans- and interdisciplinary approach combining domain knowledge, 3D machine learning, and crowdsourcingJulia Kohns0Vivien Zahs1Carolin Klonner2Bernhard Höfle3Lothar Stempniewski4Alexander Stark5Institute of Concrete Structures and Building Materials, Karlsruhe Institute of Technology (KIT), Gotthard-Franz-Straße 3, Karlsruhe 76131, Germany; Corresponding author at: Gotthard-Franz-Straße 3, 76131 Karlsruhe, Germany.3D Geospatial Data Processing (3DGeo) Research Group, Institute of Geography, Heidelberg University, Im Neuenheimer Feld 368, Heidelberg 69120, GermanyGIScience Research Group, Institute of Geography, Heidelberg University, Im Neuenheimer Feld 368, Heidelberg 69120, Germany; Department of Geography and Geology, University of Turku, Vesilinnantie 5, Turku 20500, Finland3D Geospatial Data Processing (3DGeo) Research Group, Institute of Geography, Heidelberg University, Im Neuenheimer Feld 368, Heidelberg 69120, Germany; Interdisciplinary Center for Scientific Computing (IWR), Heidelberg University, Im Neuenheimer Feld 205, Heidelberg 69120, Germany; Heidelberg Center for the Environment (HCE), Heidelberg University, Im Neuenheimer Feld 229, Heidelberg 69120, GermanyInstitute of Concrete Structures and Building Materials, Karlsruhe Institute of Technology (KIT), Gotthard-Franz-Straße 3, Karlsruhe 76131, GermanyInstitute of Concrete Structures and Building Materials, Karlsruhe Institute of Technology (KIT), Gotthard-Franz-Straße 3, Karlsruhe 76131, GermanyRecent natural disasters have claimed many lives. Reliable damage predictions and timely assessments are essential for effective rescue operation planning and efficient allocation of limited resources. Currently, experts in the field perform damage assessment manually, which is resource- and time-intensive. To address this issue, we propose a general trans- and interdisciplinary concept that combines the strengths of domain knowledge, automated computational methods, and crowdsourcing. The objective is to provide relevant and timely damage information after a natural disaster. The specific implementation presented for the earthquake damage use case includes (1) the development of a set of novel, innovative methods, (2) their combination to obtain timely and reliable damage information, (3) fully defined interfaces between all components to ensure an automated data flow, (4) implementation as a fully open-source framework, and (5) the participation of end users in the development of the framework from the beginning, contributing their expertise. Compared to other existing individual solutions, our interdisciplinary implementation has shown to provide fast and accurate information in disaster situations, aiding the management of consequences and saving lives. We consider the implementation transferable to various types of natural hazards due to its open-source realisation and the flexibility of its modules and interfaces.http://www.sciencedirect.com/science/article/pii/S2590061725000249EarthquakeDamage catalogueFragility functionAutomatic damage analysisMicro-mappingUncrewed aerial vehicle |
| spellingShingle | Julia Kohns Vivien Zahs Carolin Klonner Bernhard Höfle Lothar Stempniewski Alexander Stark Building damage assessment in natural disasters: A trans- and interdisciplinary approach combining domain knowledge, 3D machine learning, and crowdsourcing Progress in Disaster Science Earthquake Damage catalogue Fragility function Automatic damage analysis Micro-mapping Uncrewed aerial vehicle |
| title | Building damage assessment in natural disasters: A trans- and interdisciplinary approach combining domain knowledge, 3D machine learning, and crowdsourcing |
| title_full | Building damage assessment in natural disasters: A trans- and interdisciplinary approach combining domain knowledge, 3D machine learning, and crowdsourcing |
| title_fullStr | Building damage assessment in natural disasters: A trans- and interdisciplinary approach combining domain knowledge, 3D machine learning, and crowdsourcing |
| title_full_unstemmed | Building damage assessment in natural disasters: A trans- and interdisciplinary approach combining domain knowledge, 3D machine learning, and crowdsourcing |
| title_short | Building damage assessment in natural disasters: A trans- and interdisciplinary approach combining domain knowledge, 3D machine learning, and crowdsourcing |
| title_sort | building damage assessment in natural disasters a trans and interdisciplinary approach combining domain knowledge 3d machine learning and crowdsourcing |
| topic | Earthquake Damage catalogue Fragility function Automatic damage analysis Micro-mapping Uncrewed aerial vehicle |
| url | http://www.sciencedirect.com/science/article/pii/S2590061725000249 |
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