City-scale damage assessment using very-high-resolution SAR satellite imagery and building survey data for the 2021 Haiti earthquake

Conference paper (2023)

Authors

V. Macchiarulo Geo-engineering - CEG
Fatemeh Foroughnia Geo-engineering - CEG
Pietro Milillo University of Houston
Camilla Penney University of Canterbury
Keith Adams Brunel University London
Tracy Kijewski-Correa University of Notre Dame
Giorgia Giardina Geo-engineering - CEG
Research Group
Geo-engineering (CEG) (TU Delft)
Copyright: © 2023 V. Macchiarulo, Fatemeh Foroughnia, Pietro Milillo, Michael R. Z. Whitworth, Camilla Penney, Keith Adams, Tracy Kijewski-Correa, Giorgia Giardina
Remote sensing Texture analysis Post-disaster reconnaissance
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Published Date

2023-9

Language

English

Reuse Rights

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Faculty

Civil Engineering & Geosciences

Department

Geoscience and Engineering

Research Group

Geo-engineering

Abstract

After an earthquake, a rapid identification of the damaged building stock is crucial to prioritise rescue operations, ensure primary services to the most affected regions and support reconstruction. Whilst in-situ reconnaissance missions provide invaluable data on the intensity and distribution of earthquake-induced structural damage, the process of collecting field observations is often dangerous, expensive, and is usually undertaken a few weeks after the disaster. Spaceborne Synthetic Aperture Radar (SAR) can remotely provide imagery data of wide affected areas, enabling to reach locations that are difficult or dangerous to access with traditional survey methods. Furthermore, SAR-based observations are independent from daylight illumination and clear-weather conditions. Thanks to the recent availability of Very-High Resolution (VHR) SAR satellites, post-disaster imagery data with sub-metre resolution are now available within a few hours after a major earthquake, opening unprecedented opportunities for complementing in-situ operations. The textural analysis of post-earthquake VHR SAR images could be used to identify backscattering signatures that are likely associated with building damage. However, application has been limited by the lack of methods that correlate the textural properties of damaged structures in radar images with building survey data. In this paper, we present a method using textural features derived from VHR SAR post-event images in combination with building survey data to classify earthquake-induced building damage at city block-level. We tested the proposed method within the context of a joint Structural Extreme Event Reconnaissance (StEER), GeoHazards International (GHI) and Earthquake Engineering Field Investigation Team (EEFIT) mission that followed the 2021 Haiti Earthquake. The developed method was applied to the city of Les Cayes, Haiti, using a post-event Capella SAR image acquired on the 16th of August 2021. The outcomes can positively impact future earthquake scenarios, with the potential to improve rapid disaster response and remotely aid post-earthquake reconnaissance missions.