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.

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.

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.

Remote reconnaissance missions are promising solutions for the assessment of earthquake-induced structural damage and cascading geological hazards. Space-borne remote sensing can complement in-field missions when safety and accessibility concerns limit post-earthquake operations on the ground. However, the implementation of remote sensing techniques in post-disaster missions is limited by the lack of methods that combine different techniques and integrate them with field survey data. This paper presents a new approach for rapid post-earthquake building damage assessment and landslide mapping, based on Synthetic Aperture Radar (SAR) data. The proposed texture-based building damage classification approach exploits very high resolution post-earthquake SAR data integrated with building survey data. For landslide mapping, a backscatter intensity-based landslide detection approach, which also includes the separation between landslides and flooded areas, is combined with optical-based manual inventories. The approach was implemented during the joint Structural Extreme Event Reconnaissance, GeoHazards International and Earthquake Engineering Field Investigation Team mission that followed the 2021 Haiti Earthquake and Tropical Cyclone Grace.

On 14th August 2021, a magnitude 7.2 earthquake struck the Tiburon Peninsula in the Caribbean nation of Haiti, approximately 150 km west of the capital Port-au-Prince. Aftershocks up to moment magnitude 5.7 followed and over 1,000 landslides were triggered. These events led to over 2,000 fatalities, 15,000 injuries and more than 137,000 structural failures. The economic impact is of the order of US$1.6 billion. The on-going Covid pandemic and a complex political and security situation in Haiti meant that deploying earthquake engineers from the UK to assess structural damage and identify lessons for future building construction was impractical. Instead, the Earthquake Engineering Field Investigation Team (EEFIT) carried out a hybrid mission, modelled on the previous EEFIT Aegean Mission of 2020. The objectives were: to use open-source information, particularly remote sensing data such as InSAR and Optical/Multispectral imagery, to characterise the earthquake and associated hazards; to understand the observed strong ground motions and compare these to existing seismic codes; to undertake remote structural damage assessments, and to evaluate the applicability of the techniques used for future post-disaster assessments. Remote structural damage assessments were conducted in collaboration with the Structural Extreme Events Reconnaissance (StEER) team, who mobilised a group of local non-experts to rapidly record building damage. The EEFIT team undertook damage assessment for over 2,000 buildings comprising schools, hospitals, churches and housing to investigate the impact of the earthquake on building typologies in Haiti. This paper summarises the mission setup and findings, and discusses the benefits, and difficulties, encountered during this hybrid reconnaissance mission.

Post-earthquake reconnaissance missions are critical to understand the event characteristics, identify building and infrastructure vulnerabilities, and improve future construction practice. However, in-field missions can present logistic and safety challenges that do not make them viable in every post-disaster scenario. Remote sensing technique can be used to rapidly collect a large amount information that can be used to enrich the post-event learning process. While the possibility to deploy teams in the field remain a valuable asset for an integrated understanding of technical and socio-economic factors, a mix of remote and in-field reconnaissance activities can be a way forward in post-disaster management.

This work presents the results of a hybrid mission mobilised by the Earthquake Engineering Field Investigation Team (EEFIT) after the 2021 Haiti earthquake. On 14 August 2021, a 7.2 magnitude earthquake struck the Tiburon Peninsula in the Caribbean nation of Haiti, approximately 150km east of the capital Port au Prince. The event was followed by numerous aftershocks up to magnitude 5.7, and tiggered over 1000 landslides. Over 2000 people lost their lives, with over 15,000 injured and over 137,000 houses damaged or destroyed. The estimated economic impact is of the order of US$1.6 billion. Due the complex political and security situation in Haiti, coupled with the global pandemic, a full in field mission was not considered feasible, so a hybrid mission was designed instead.

First, open-source information was collected and used to characterise the seismic event, analyse the strong ground motion and compare to established national and international earthquake codes and standard. Second, remote sensing techniques including Interferometric Synthetic Aperture Radar (InSAR) and Optical/Multispectral imagery were used to understand the earthquake mechanism, the ground displacement distribution and the possibility to detect landslide on a regional scale. The general applicability of remote sensing technique in the context of post disaster assessment was also evaluated. Finally, the earthquake impact on different building typologies in Haiti was investigated through the damage assessment of over 2000 buildings comprising schools, hospitals, churches and housing. This was done in collaboration with the Structural Extreme Events Reconnaissance (StEER) team, who mobilised a team of local non-experts to rapidly record building damage.

This talk summarises the mission setup and findings, and discusses the benefits of and difficulties encountered during this hybrid reconnaissance.