Print Email Facebook Twitter Integrating post-event very high resolution SAR imagery and machine learning for building-level earthquake damage assessment Title Integrating post-event very high resolution SAR imagery and machine learning for building-level earthquake damage assessment Author Macchiarulo, V. (TU Delft Geo-engineering) Giardina, Giorgia (TU Delft Geo-engineering) Milillo, Pietro (University of Houston; Deutsches Zentrum für Luft- und Raumfahrt e.V. (DLR)) Aktas, Yasemin D. (University College London) Whitworth, Michael R.Z. (AECOM Technology Corporation, United Kingdom) Date 2024 Abstract Earthquakes have devastating effects on densely urbanised regions, requiring rapid and extensive damage assessment to guide resource allocation and recovery efforts. Traditional damage assessment is time-consuming, resource-intensive, and faces challenges in covering vast affected areas, often limiting timely decision-making. Space-borne synthetic aperture radars (SAR) have gained attention for their all-weather and day-night imaging capabilities. These advantages, coupled with wide coverage, short revisits and very high resolution (VHR), have created opportunities for using SAR data in disaster response. However, most SAR studies for post-earthquake damage assessment rely on change detection methods using pre-event SAR images, which are often unavailable in operational scenarios. Limited studies using solely post-event SAR data primarily concentrate on city-block-level damage assessment, thus not fully exploiting the VHR SAR potential. This paper presents a novel method integrating solely post-event VHR SAR imagery and machine learning (ML) for regional-scale post-earthquake damage assessment at the individual building-level. We first used supervised learning on case-specific datasets, and then introduced a combined learning approach, incorporating inventories from multiple case studies to assess generalisation. Finally, the ML model was tested on unseen study areas, to evaluate its flexibility in unfamiliar contexts. The method was implemented using datasets collected during the Earthquake Engineering Field Investigation Team (EEFIT) reconnaissance missions following the 2021 Nippes earthquake and the 2023 Kahramanmaraş earthquake sequence. The results demonstrate the method’s ability to classify standing and collapsed buildings, achieving up to 72% overall accuracy on unseen regions. The proposed method has potential for future disaster assessments, thereby contributing to more effective earthquake management strategies. Subject Disaster managementML techniquesPost-earthquake reconnaissanceRemote sensingSynthetic aperture radarTexture analysis To reference this document use: http://resolver.tudelft.nl/uuid:cc6842c0-23d3-40c6-be10-f93f8dee55fc DOI https://doi.org/10.1007/s10518-024-01877-1 ISSN 1570-761X Source Bulletin of Earthquake Engineering Part of collection Institutional Repository Document type journal article Rights © 2024 V. Macchiarulo, Giorgia Giardina, Pietro Milillo, Yasemin D. Aktas, Michael R.Z. Whitworth Files PDF s10518-024-01877-1.pdf 5.19 MB Close viewer /islandora/object/uuid:cc6842c0-23d3-40c6-be10-f93f8dee55fc/datastream/OBJ/view