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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 i, Geo-engineering

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., Geo-engineering

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., Geo-engineering

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, Geo-engineering

Mitigation of losses due to coastal hazards has become an increasingly urgent and challenging problem in light of rising seas and the continued escalation of coastal population density. Unfortunately, stakeholders responsible for assuring the safety of these coastal communities are not equipped with the engineering research community’s latest tools for high-fidelity risk assessment and geospatial decision support. In the event of a hurricane or nor’easter, such capabilities are exceptionally vital to project storm impacts on critical infrastructure and other municipal assets and to inform preemptive actions that can save lives and mitigate property damage. In response, a web-based visualization environment was developed using the GeoNode content management system, informed by the needs of municipal stakeholders. Within this secure platform, registered users with roles in planning, emergency management and first response can simulate the impact of hurricanes and nor’easters using the platform’s storm Hazard Projection (SHP) Tool. The SHP Tool integrates fast-to-compute windfield models with surrogate models of high-fidelity storm surge and waves to rapidly simulate user-defined storm scenarios, considering the effects of tides, sea level rise, dune breaches and track uncertainty. In the case of a landfalling hurricane, SHP tool outputs are automatically loaded into the user’s dashboard to visualize the projected wind, storm surge and wave run-up based on the latest track information published by the National Hurricane Center. Under either use case, outputs of the SHP Tool are visualized within a robust collaborative geospatial environment supporting the seamless exploration of centralized libraries of geographic information system (GIS) data from federal, state, county and local authorities, with tools to add user-supplied annotations such as notes or other geospatial mark-ups. This paper will overview the development and deployment of this platform in the State of N

Numerical advances in storm surge prediction over the past couple of decades have produced high-fidelity simulation models that permit a detailed representation of hydrodynamic processes and therefore support high accuracy forecasting. Unfortunately, the computational burden of such numerical models is large, requiring thousands of CPU hours for each simulation, something that limits their applicability for hurricane risk assessment. Use of Kriging-based surrogate modeling techniques has been examined to address the aforementioned challenge Jia et al. [2016], Zhang et al. [2018]. This approach can provide fast predictions using a database of high-fidelity, synthetic storms, with the goal of maintaining the accuracy of the numerical model utilized to produce this database, while offering computational efficiency. This contribution overviews initially recent research developments for the application of Kriging for storm surge predictions. Topics discussed include: enhancement of the initial database for nodes (i.e., geographical locations) that have remained dry in some of the database storms; adaptive selection of storms forming the initial database; use of different surrogate modeling tuning techniques and their impact on the metamodel predictive capabilities for storm surge estimation; implementation for estimation of impact due to near-shore processes (breaking waves), something that requires coupling of different numerical models.