InSAR-based assessment of post-earthquake building reconstruction

Abstract

Evaluating long-term building reconstruction is essential to strengthen resilience to earthquakes. Field investigations provide detailed and accurate information for building assessments, but are often labour intensive, costly, and time consuming, particularly when considering the regional-scale impact of earthquakes. In contrast, satellite Remote Sensing (RS) techniques provide frequent data across vast areas, making them ideal for regional-scale post-earthquake assessments, which can complement field surveys. Despite this, most RS studies have relied on manual change detection of satellite data before and after the event, limiting their potential for automated assessment and reducing their support for field investigations. In this study, we developed a novel RS method designed to assist field investigations of post-earthquake building reconstruction on a regional scale. The method automatically identifies target buildings for field teams to investigate, locating collapsed structures or buildings that have changed due to post-earthquake reconstruction efforts. We applied Multi-Temporal Synthetic Aperture Radar Interferometry (MT-InSAR) for the first time to evaluate post-earthquake building reconstruction. The proposed method involves a two-stage analysis: first, a grid-level assessment on a regional scale to detect areas with reconstruction activities following an earthquake, and then a detailed building-level analysis to identify individual buildings that have undergone changes as part of the reconstruction process within these areas. The method was used to assess building reconstruction efforts in Nepal after the 2015 Gorkha earthquake. For the MT-InSAR analysis, we acquired two stacks of 3-m-resolution SAR images, one before and one after the earthquake. The grid-level analysis detected multiple urban areas with significant changes, which were then subjected to a building-level analysis. This analysis pinpointed the locations of affected buildings and determined the extent of changes related to reconstruction activities. A comparison of the building-level results with field observations confirmed that the method successfully identified buildings that have undergone changes. These changes included buildings that were left in a collapsed state, demolished, under construction, or fully reconstructed. The MT-InSAR-based approach introduced in this study has the potential to serve as a valuable tool to guide future field surveys related to post-earthquake reconstruction, significantly reducing the time and effort needed for such assessment.