Predicting the availability of measurement points provided by Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR) poses a challenge due to a nonuniform distribution of Persistent Scatterers (PSs). This article introduces a novel method to estimate the availability of MT-InSAR results on buildings and infrastructure networks, eliminating the need for labor-intensive and time-consuming analyses of the entire SAR data stack. The method is based on an analysis of the interferometric coherence decay characteristics and data regarding buildings and transport infrastructure location as inputs to a convolutional neural network. Specifically, a U-Net architecture model was implemented and trained to predict the PS density of Sentinel-1 data. The methodology was applied to a regional-scale analysis of the Dutch infrastructure, resulting in a low 1.06pm0.10 mean absolute error in the pixel-based PS count estimation on the test data split, with over 80% of predictions within pm1 from the actual value. The model achieved high accuracy when applied to a previously unseen dataset, demonstrating strong generalization performance. The proposed workflow, with its notable ability to accurately predict areas lacking measurement points, offers stakeholders a tool to assess the feasibility of applying MT-InSAR for specific structures. Thereby, it enhances infrastructure reliability by addressing a critical need in decision-making processes and improving the applicability of MT-InSAR for structural health monitoring of infrastructure assets.

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Thousands of bridges worldwide face growing risks due to aging materials, increased traffic loads, and climate change-induced weather extremes. Managing these assets is financially demanding, and requires prioritisation strategies for interventions. Consequently, innovative approaches are urgently required to evaluate the structural conditions of these bridges continuously and regularly. Recent advancements in space-borne Interferometric Synthetic Aperture Radar (InSAR) technology offer cost-effective remote monitoring capabilities, ensuring extensive coverage and high spatial resolution. Multi Temporal (MT) InSAR techniques enable the reconstruction of millimetre-scale deformation measurements for a large number of assets, opening opportunities for long-term regional-scale monitoring of bridge deformations. However, a major challenge in utilising MT-InSAR-based displacement data operationally is that MT-InSAR analysis reconstructs only the projection of displacements along the satellite Line of Sight (LOS) direction. Due to the typical availability of only two satellite viewing geometries, in most cases the three-dimensional displacement field cannot be fully reconstructed. Consequently, without accounting for the anticipated motion of a given structure and its alignment with respect to the satellite flight path, the actual asset movement is likely to be underestimated, leading to erroneous interpretation. In this paper, we propose a method using the bridge typologies and their associated likely failure mechanisms to derive assumptions regarding expected displacement directions. Then, the information on bridge alignments with respect to the satellite flight direction is used to assess the MT-InSAR sensitivity to the expected displacement directions and define ad-hoc damage indicators. We tested the proposed method on urban bridges in Amsterdam, the Netherlands, using deformation measurements derived from TerraSAR-X data spanning 2016 to 2020. Findings have potential to enhance current procedures for the structural evaluation of bridges.

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The Kakhovka Dam on the Dnieper River in Kherson Oblast, Ukraine, was completed in 1956 as the final dam in the Dnieper reservoir cascade. On the morning of June 6^th, 2023, a substantial portion of the dam suffered a collapse while under Russian control. This incident was documented through satellite optical and radar images, providing valuable evidence of the dam’s condition. Here we present the results of multi-temporal Interferometric Synthetic Aperture Radar (MT-InSAR) monitoring of the Kakhovka dam. The dam is vital for water management and hydroelectric power generation. Utilizing multi-temporal InSAR (MT-InSAR) data, we assessed the dam deformations prior to the collapse. Our findings indicate movements of the south side, facing the Dniprovska Gulf, compatible with several possible damage mechanisms. This study highlights the significance of employing spaceborne advanced monitoring techniques to detect signs of distress and ensure the stability of critical infrastructure.

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The implementation of effective and sustainable Structural Health Monitoring (SHM) systems for the evaluation of infrastructure conditions is critical to address the deterioration and damage experienced by structures worldwide. Given the vast number of structures involved, resorting to traditional in-situ visual inspections and data gathering methods is becoming increasingly unfeasible. Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR) has recently gained attention as a viable solution for long-term SHM. This remote sensing technique combines multiple satellite radar images to measure changes in the Earth’s surface over time. Unlike conventional techniques, MT-InSAR does not require in-situ installations and offers extensive coverage, enabling observations across diverse location and structures. However, the applicability of MT-InSAR monitoring depends on the relatively unpredictable distribution and location of permanent scatterers (PSs), which are influenced by surface characteristics and vegetation changes. Evaluating the reliability and capacity of MT-InSAR is therefore crucial to enhance its effectiveness in assessing the location and extent of structural damage. In this study, we present an effective approach to determine the optimal number and position of PSs for detecting different structural damage mechanisms. The approach is exemplified through a case study of a quay wall in Amsterdam, with data inputs simulated using the Finite Element Method. The proposed method has the potential to evaluate the feasibility of MT-InSAR for a broader range of scenarios, enabling to detect specific structural conditions.@en

Accurate and rapid postearthquake structural damage assessment is of vital importance for humanitarian relief. Remote sensing techniques have the potential to map large areas with reduced data latency but are limited by several factors, including accuracy (compared to in-situ monitoring campaigns) and data acquisition frequency. Current damage assessment techniques relying on remote sensing data enable rapid assessment in situations where on-site reconnaissance is not possible or desirable. Yet, these techniques rely on different scales, measurement methods, and spatial resolutions, making it difficult to assimilate many different damage products in a homogeneous damage map. Here, we present the results of the U.K.'s Earthquake Engineering Field Investigation Team's remote-sensing-based reconnaissance mission, which was carried out in the aftermath of the series of earthquakes that struck Turkey and Syria in February 2023. We use a set of publicly available damage maps based on synthetic aperture radar, optical imaging, and ground-based reports as well as in-house developed damage products and assess their relative accuracies. We describe the process of supporting on-site reconnaissance planning by creating maps that describe the building stock and diversity of damage in southeast Turkey to assist field survey teams in selecting regions that represent a diverse sample of building typologies and damage levels. Our results show that satellite-based remote sensing damage maps disagree with each other, and extensive validation data are still required to characterize the accuracy of each method at both high and medium resolution. Finally, we provide recommendations for planning and validation of future earthquake response efforts.

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Regional-scale assessment of the damage caused by earthquakes to structures is crucial for post-disaster management. While remote sensing techniques can be of great help for a quick post-event structural assessment of large areas, currently available methods are limited to the detection of severely-damaged buildings. Furthermore, remote sensing-based assessment methods typically provide only qualitative results, as they lack integration with information on the building's behaviour in response to seismic-induced ground shaking. In this study, we developed a new methodology that uses airborne Light Detection And Ranging (LiDAR) data in combination with structural indicators of building response to provide a quantitative assessment of earthquake-induced damage at a regional scale. LiDAR datasets collected before and after an earthquake are used to measure residual displacements of building roofs. The resulting lateral drift estimations are used to quantify the level of damage for a specific building typology. Application to the LiDAR datasets collected before and after the 2014 earthquake in Napa Valley, California, demonstrates the capability of the proposed method to detect moderate levels of structural damage, proving its potential for faster and more accurate support to post-disaster management.

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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.

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Bridges play a vital role in the European transport network, and their preservation is of utmost importance. Despite many centuries- old bridges still being in use in European cities, their structural integrity may be compromised due to factors like material degradation, increased traffic loads, extreme events, or slow deformation phenomena. It is essential to regularly assess the current conditions of these structures and monitor their evolution over time to enable timely intervention when necessary. This study presents the first results of a multidisciplinary methodology for the Structural Health Monitoring (SHM) of typical urban bridges in the Netherlands, combining numerical simulations using the Applied Element Method (AEM) with monitoring data derived from various sensing sources. These sources range from standard in situ techniques to satellite remote sensing using Synthetic Aperture Radar Interferometry (InSAR). The methodology is applied to a representative bridge of Amsterdam canals. The nonlinear analyses have led to a numerically predicted crack pattern consistent with on-site observations. The simulated damage progression until collapse identifies critical points of the bridge to be kept under control with monitoring activities.

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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.@en

Structural health monitoring (SHM) is the automation of the condition assessment process of an engineered system. When applied to geometrically large components or structures, such as those found in civil and aerospace infrastructure and systems, a critical challenge is in designing the sensing solution that could yield actionable information. This is a difficult task to conduct cost-effectively, because of the large surfaces under consideration and the localized nature of typical defects and damages. There have been significant research efforts in empowering conventional measurement technologies for applications to SHM in order to improve performance of the condition assessment process. Yet, the field implementation of these SHM solutions is still in its infancy, attributable to various economic and technical challenges. The objective of this Roadmap publication is to discuss modern measurement technologies that were developed for SHM purposes, along with their associated challenges and opportunities, and to provide a path to research and development efforts that could yield impactful field applications. The Roadmap is organized into four sections: distributed embedded sensing systems, distributed surface sensing systems, multifunctional materials, and remote sensing. Recognizing that many measurement technologies may overlap between sections, we define distributed sensing solutions as those that involve or imply the utilization of numbers of sensors geometrically organized within (embedded) or over (surface) the monitored component or system. Multi-functional materials are sensing solutions that combine multiple capabilities, for example those also serving structural functions. Remote sensing are solutions that are contactless, for example cell phones, drones, and satellites. It also includes the notion of remotely controlled robots.

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Earthquakes are natural hazards leading to the greatest human and economic losses, which are mostly due to structural collapses. Rapid identification and assessment of earthquake-induced damage to structures is therefore an essential component of the emergency response, and instrumental to effective reconstruction plans. Typically, structural damage assessment is conducted through building-by-building inspections during post-earthquake field reconnaissance missions. These missions are expensive and time-consuming, especially if large areas need to be investigated. Remote sensing techniques provide a relatively low-cost, wide-area alternative to in-situ monitoring. Classification and change detection based on pre- and post-event optical and synthetic aperture radar (SAR) satellite images are the most used approaches to detect damaged structures after earthquakes. However, these techniques only provide qualitative observations of collapsed or severely damaged structures. In this work, we present a new approach for the quantitative assessment of earthquake-induced structural damage based on displacement measurements acquired by Airborne Light Detection And Ranging (LiDAR). The approach is based on the integration between LiDAR-based observations and structural indicators of damage. The application to the analysis of 684 buildings affected by the 2014 Napa earthquake, in California, demonstrates a good agreement between the LiDAR-based results and independent in-situ observations. This work sets the basis for the innovative exploitation of remote sensing data in disaster management. @en

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.

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Worldwide, countries are facing the challenge of ageing transport infrastructure, as thousands of assets have already reached the end of their life service. Structural monitoring is crucial to identify damage precursors and prevent structural failure, but the health evaluation of so many assets is challenging. Space-borne Interferometric Synthetic Aperture Radar (InSAR) can remotely provide high-resolution and high-density monitoring data over large areas, allowing to reconstruct the displacement field of structures with millimetre-scale accuracy. InSAR displacement measurements have been widely used within the civil engineering field, demonstrating that this technology can detect building and infrastructure deformations. However, to use satellite datasets for structural-monitoring purpose, (i) InSAR displacement measurements need to be related to large infrastructure inventories and (ii) performance indicators for the identification of structural anomalies on a large scale need to be defined. We present a novel methodology based on the automated integration of InSAR-derived displacements with infrastructure databases for the identification of early warnings over large networks. The proposed methodology is applied to the Italian motorway network and bridges. The proposed methodology leads to the creation of risk maps highlighting the assets which exhibit the most rapid variation in monitored deformations and anomalous differential movements within the infrastructure@en

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.@en

Worldwide, transport infrastructure is increasingly vulnerable to aging-induced deterioration and climate-related hazards. Often, inspection and maintenance costs far exceed the available resources, and numerous assets lack any rigorous structural evaluation. Space-borne synthetic aperture radar interferometry (InSAR) is a powerful remote sensing technology that can provide cheaper deformation measurements for bridges and other transport infrastructure with short revisit times, while scaling from the local to the global scale. As recent studies have shown InSAR accuracy to be comparable to that of traditional monitoring instruments, InSAR could offer a cost-effective tool for long-term, near-continuous deformation monitoring, with the possibility of supporting inspection planning and maintenance prioritisation while maximising functionality and increasing the resilience of infrastructure networks. However, despite the high potential of InSAR for structural monitoring, some important limitations need to be considered when applying it in practice. In this paper, the challenges of using InSAR for the purpose of structural monitoring are identified and discussed, with specific focus on bridges and transport networks. Examples are presented to illustrate the current practical limitations of InSAR, and possible solutions and promising research directions are identified. The aim of the paper is to motivate future action in this area and highlight the InSAR advances needed to overcome current challenges.

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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.@en

Ageing stock and extreme weather events pose a threat to the safety of infrastructure networks. In most countries, funding allocated to infrastructure management is insufficient to perform systematic inspections over large transport networks. As a result, early signs of distress can develop unnoticed, potentially leading to catastrophic structural failures. Over the past 20 years, a wealth of literature has demonstrated the capability of satellite-based Synthetic Aperture Radar Interferometry (InSAR) to accurately detect surface deformations of different types of assets. Thanks to the high accuracy and spatial density of measurements, and a short revisit time, space-borne remote-sensing techniques have the potential to provide a cost-effective and near real-time monitoring tool. Whilst InSAR techniques offer an effective approach for structural health monitoring, they also provide a large amount of data. For civil engineering procedures, these need to be analysed in combination with large infrastructure inventories. Over a regional scale, the manual extraction of InSAR-derived displacements from individual assets is extremely time-consuming and an automated integration of the two datasets is essential to effectively assess infrastructure systems. This paper presents a new methodology based on the fully automated integration of InSAR-based measurements and Geographic Information System-infrastructure inventories to detect potential warnings over extensive transport networks. A Sentinel dataset from 2016 to 2019 is used to analyse the Los Angeles highway and freeway network, while the Italian motorway network is evaluated by using open access ERS/Envisat datasets between 1992 and 2010, COSMO-SkyMed datasets between 2008 and 2014 and Sentinel datasets between 2014 and 2020. To demonstrate the flexibility of the proposed methodology to different SAR sensors and infrastructure classes, the analysis of bridges and viaducts in the two test areas is also performed. The outcomes highlight the potential of the proposed methodology to be integrated into structural health monitoring systems and improve current procedures for transport network management.

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In the planning stage of new infrastructure or when designing renovation of existing infrastructure, information about existing slope movements or settlements is essential to make informed design decisions. Interferometric
Synthetic Aperture Radar (InSAR) techniques can be of value to identify these risks in an early stage of a project. InSAR can offer insight into the surface movements of an area from historic archives using satellite-based SAR
data. Furthermore, InSAR observations can help identify zones with displacements larger than the average of an area, and be used to plan future soil investigation more effectively. Thanks to their high temporal and spatial resolution, InSAR observations can also complement in situ conventional monitoring during the construction and operational stage. Despite these possibilities, the use of InSAR is not yet standard practice in geotechnical projects
and no formal guidelines are currently available to inform engineers, planners and infrastructure stakeholder on the use of InSAR-based monitoring within geotechnical design codes. Here we provide an operational framework for the practical integration of InSAR monitoring into current geotechnical design codes, such as Eurocode-7, for all project stages. The proposed framework is then demonstrated for the planning stage of a highway renovation project, focusing on an area potentially subjected to landslides where no conventional monitoring
data was available at this stage. We concluded that the proposed framework is a practical and operational
tool that can be used by planners and engineers in the whole lifecycle of an infrastructure project.@en

This paper proposes an automated approach to predict crack pattern similarities that correlate well with assessment by structural engineers. We use Siamese convolutional neural networks (SCNN) that take two crack pattern images as inputs and output scalar similarity measures. We focus on 2D masonry facades with and without openings. The image pairs are generated using a statistics-based approach and labelled by 28 structural engineering experts. When the data is randomly split into fit and test data, the SCNNs can achieve good performance on the test data ((Formula presented.)). When the SCNNs are tested on ”unseen” archetypes, their test (Formula presented.) values are on average 1% lower than the case where all archetypes are ”seen” during the training. These very good results indicate that SCNNs can generalise to unseen cases without compromising their performance. Although the analyses are restricted to the considered synthetic images, the results are promising and the approach is general.

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