Unsupervised structural damage assessment from space using the segment anything model (USDA-SAM): A case Study of the 2023 Turkiye earthquake

Balaji, Sudharshan and Karakus, Oktay ORCID: https://orcid.org/0000-0001-8009-9319 2024. Unsupervised structural damage assessment from space using the segment anything model (USDA-SAM): A case Study of the 2023 Turkiye earthquake. Presented at: International Geoscience and Remote Sensing Symposium, Athens, Greece, 07-12 July 2024. IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium. IEEE, pp. 585-589. 10.1109/IGARSS53475.2024.10640396

Preview

PDF - Accepted Post-Print Version Download (6MB) | Preview

Abstract

This paper explores advanced deep learning methods, specifically utilising the Segment Anything Model (SAM) along with image processing techniques, to evaluate the structural damages caused by the devastating earthquake that occurred in Turkey on February 6, 2023. Leveraging exceptionally high-resolution pre- and post-disaster imagery provided by Maxar Technologies, this paper showcases the efficacy of SAM in contrasting and quantifying the magnitude of structural devastation. The proposed unsupervised structural damage assessment (USDA-SAM) method entails a thorough comparative analysis of aerial imagery captured both before and after the seismic event, facilitating a nuanced evaluation of its impact on buildings and critical infrastructure. USDA-SAM also proposes two metrics - damage assessment score (DAS) and affected number of buildings (N b/km 2 ) - to quantitatively measure the damage caused by the disasters. The study highlights the transformative potential of deep learning and image processing, shedding light on their key role in fortifying disaster response strategies and emphasising technology’s indispensable contribution to mitigating the challenges posed by natural disasters, such as earthquakes.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Published Online
Status:	Published
Schools:	Computer Science & Informatics
Publisher:	IEEE
ISBN:	9798350360325
Funders:	N/A
Date of First Compliant Deposit:	10 October 2024
Date of Acceptance:	5 September 2024
Last Modified:	15 Nov 2024 02:45
URI:	https://orca.cardiff.ac.uk/id/eprint/172839

Actions (repository staff only)

Edit Item