Abstract
AbstractEarthquakes 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.
Funder
Dutch Research Council
Commercial Smallsat Data Scientific Analysis Program of NASA
Engineering and Physical Sciences Research Council
Publisher
Springer Science and Business Media LLC
Reference74 articles.
1. AFAD (2023) Press Bulletin -36 about the earthquake in Kahramanmaraş— Turkish disaster and emergency management presidency. https://en.afad.gov.tr/press-bulletin-36-about-the-earthquake-in-kahramanmaras
2. Aktas Y, So E, Johnson C, et al (2023) hybrid EEFIT mission to February 2023 KahramanmaraŞ earthquake sequence. In: SECED 2023 Conference-earthquake engineering and dynamics for a sustainable future
3. Aktas YD, Ioannou I, Malcioglu FS et al (2022) Hybrid reconnaissance mission to the 30 October 2020 Aegean Sea earthquake and tsunami (Izmir, Turkey and Samos, Greece): description of data collection methods and damage. Front Built Environ 8:1–24. https://doi.org/10.3389/fbuil.2022.840192
4. Bai Y, Adriano B, Mas E et al (2017) Building damage assessment in the 2015 Gorkha, Nepal, earthquake using only post-event dual polarization synthetic aperture radar imagery. Earthq Spectra 33(1–suppl):185–195. https://doi.org/10.1193/121516eqs232m
5. Balz T, Liao M (2010) Building-damage detection using post-seismic high-resolution SAR satellite data. Int J Remote Sens 31(13):3369–3391. https://doi.org/10.1080/01431161003727671