Towards Real-Time Building Damage Mapping with Low-Cost UAV Solutions

Nex, Francesco; Duarte, Diogo; Steenbeek, Anne; Kerle, N.

doi:10.3390/rs11030287

Cited by 91 publications

(57 citation statements)

References 34 publications

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“…However, the aim of this work was to test the suitability of multi-temporal satellite images that potentially allows a synoptic recovery assessment at any desired time interval, while both the availability and spatial coverage of UAV data are typically limited. In addition, recent work on structural damage mapping with UAV has also demonstrated the limits of those data [76], even when advanced deep learning methods are used [77].…”

Section: Discussionmentioning

confidence: 99%

Evaluating Resilience-Centered Development Interventions with Remote Sensing

Kerle

Ghaffarian

Nawrotzki³

et al. 2019

Remote Sensing

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Natural disasters are projected to increase in number and severity, in part due to climate change. At the same time a growing number of disaster risk reduction (DRR) and climate change adaptation measures are being implemented by governmental and non-governmental organizations, and substantial post-disaster donations are frequently pledged. At the same time there has been increasing demand for transparency and accountability, and thus evidence of those measures having a positive effect. We hypothesized that resilience-enhancing interventions should result in less damage during a hazard event, or at least quicker recovery. In this study we assessed recovery over a 3 year period of seven municipalities in the central Philippines devastated by Typhoon Haiyan in 2013. We used very high resolution optical images (<1 m), and created detailed land cover and land use maps for four epochs before and after the event, using a machine learning approach with extreme gradient boosting. The spatially and temporally highly variable recovery maps were then statistically related to detailed questionnaire data acquired by DEval in 2012 and 2016, whose principal aim was to assess the impact of a 10 year land-planning intervention program by the German agency for technical cooperation (GIZ). The survey data allowed very detailed insights into DRR-related perspectives, motivations and drivers of the affected population. To some extent they also helped to overcome the principal limitation of remote sensing, which can effectively describe but not explain the reasons for differential recovery. However, while a number of causal links between intervention parameters and reconstruction was found, the common notion that a resilient community should recover better and more quickly could not be confirmed. The study also revealed a number of methodological limitations, such as the high cost for commercial image data not matching the spatially extensive but also detailed scale of field evaluations, the remote sensing analysis likely overestimating damage and thus providing incorrect recovery metrics, and image data catalogues especially for more remote communities often being incomplete. Nevertheless, the study provides a valuable proof of concept for the synergies resulting from an integration of socio-economic survey data and remote sensing imagery for recovery assessment.

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Section: Discussionmentioning

confidence: 99%

Evaluating Resilience-Centered Development Interventions with Remote Sensing

Kerle

Ghaffarian

Nawrotzki³

et al. 2019

Remote Sensing

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“…However, in particular satellite images have been shown to have severe limitations in damage mapping (Kerle, 2010), mainly due to their comparatively limited spatial detail (resolution is at best cm for commercial imagery), but also their vertical perspective that severely limits the damage evidence that can be detected. Damage data can also be provided by drones, which yield more local observations that can be incorporated further in 3D modelling of the areas (Nex et al, 2019;Kerle et al, 2019a;. In particular, advances in machine learning have led to methods for accurate damage identification from drone data (Nex et al, 2019;Kerle et al, 2019a).…”

Section: Engineering-based Measures A) Emerging Techniques In Pre/posmentioning

confidence: 99%

“…Damage data can also be provided by drones, which yield more local observations that can be incorporated further in 3D modelling of the areas (Nex et al, 2019;Kerle et al, 2019a;. In particular, advances in machine learning have led to methods for accurate damage identification from drone data (Nex et al, 2019;Kerle et al, 2019a).…”

Section: Engineering-based Measures A) Emerging Techniques In Pre/posmentioning

confidence: 99%

Towards Resilient Vital Infrastructure Systems: Challenges, Opportunities, and Future Research Agenda

Mehvar

Wijnberg

Borsje

et al. 2020

Preprint

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Abstract. Infrastructure systems are inextricably tied to society by providing a variety of vital services. These systems play a fundamental role in reducing the vulnerability of communities and increasing their resilience to natural and human-induced hazards. While diverse definitions of the resilience engineering concept exist for the infrastructures, analysing resilience of these systems within cross sectoral and interdisciplinary perspectives remains limited and fragmented in research and practice. This review synthesizes and complements existing knowledge in designing resilient vital infrastructures with the aim to assist researchers and policy makers by identifying: (1) key conceptual tensions and challenges that arise when designing resilient infrastructure systems; (2) engineering and non-engineering based measures to enhance resilience of the vital infrastructures, including the best recent practices available; and (3) opportunities for future research in this field. Results from a systematic literature review combined with expert interviews are integrated into a conceptual framework in which infrastructures are defined as a conglomeration of interdependent social, ecological, and technical systems. Our results indicate that conceptual and practical challenges in designing resilient infrastructures continue to exist, hence these systems are still being built without taking resilience explicitly into account. A review of available measures and recent applications shows that these measures have not been widely applied in designing different systems. To advance our understanding of the resilience engineering concept for infrastructure systems, main pressing topics to address evolve around the: (i) integration of the combined social, ecological and technical resilience of infrastructure systems, focusing on cascading effects of failures and dependencies across these complex systems; and (ii) development of new technology to identify the factors that create different recovery characteristics for these socio-ecological-technical systems.

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“…We built a smart phone app that allows this procedure to be executed together with a standard laptop ( Figure 5). Details about the app and data processing workflow can be found in Nex et al, (2019).…”

Section: Pilot Experimentsmentioning

confidence: 99%

Uav-Based Structural Damage Mapping – Results From 6 Years Of Research in Two European Projects

Kerle

Nex

Duarte

et al. 2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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<p><strong>Abstract.</strong> Structural disaster damage detection and characterisation is one of the oldest remote sensing challenges, and the utility of virtually every type of active and passive sensor deployed on various air- and spaceborne platforms has been assessed. The proliferation and growing sophistication of UAV in recent years has opened up many new opportunities for damage mapping, due to the high spatial resolution, the resulting stereo images and derivatives, and the flexibility of the platform. We have addressed the problem in the context of two European research projects, RECONASS and INACHUS. In this paper we synthesize and evaluate the progress of 6 years of research focused on advanced image analysis that was driven by progress in computer vision, photogrammetry and machine learning, but also by constraints imposed by the needs of first responder and other civil protection end users. The projects focused on damage to individual buildings caused by seismic activity but also explosions, and our work centred on the processing of 3D point cloud information acquired from stereo imagery. Initially focusing on the development of both supervised and unsupervised damage detection methods built on advanced texture features and basic classifiers such as Support Vector Machine and Random Forest, the work moved on to the use of deep learning. In particular the coupling of image-derived features and 3D point cloud information in a Convolutional Neural Network (CNN) proved successful in detecting also subtle damage features. In addition to the detection of standard rubble and debris, CNN-based methods were developed to detect typical façade damage indicators, such as cracks and spalling, including with a focus on multi-temporal and multi-scale feature fusion. We further developed a processing pipeline and mobile app to facilitate near-real time damage mapping. The solutions were tested in a number of pilot experiments and evaluated by a variety of stakeholders.</p>

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Towards Real-Time Building Damage Mapping with Low-Cost UAV Solutions

Cited by 91 publications

References 34 publications

Evaluating Resilience-Centered Development Interventions with Remote Sensing

Evaluating Resilience-Centered Development Interventions with Remote Sensing

Towards Resilient Vital Infrastructure Systems: Challenges, Opportunities, and Future Research Agenda

Uav-Based Structural Damage Mapping – Results From 6 Years Of Research in Two European Projects

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