Structural Building Damage Detection with Deep Learning: Assessment of a State-of-the-Art CNN in Operational Conditions

Remotely sensed data can provide the basis for timely and efficient building damage maps that are of fundamental importance to support the response activities following disaster events. However, the generation of these maps continues to be mainly based on the manual extraction of relevant information in operational frameworks. Considering the identification of visible structural damages caused by earthquakes and explosions, several recent works have shown that Convolutional Neural Networks (CNN) outperform traditional methods. However, the limited availability of publicly available image datasets depicting structural disaster damages, and the wide variety of sensors and spatial resolution used for these acquisitions (from space, aerial and UAV platforms), have limited the clarity of how these networks can effectively serve First Responder needs and emergency mapping service requirements. In this paper, an advanced CNN for visible structural damage detection is tested to shed some light on what deep learning networks can currently deliver, and its adoption in realistic operational conditions after earthquakes and explosions is critically discussed. The heterogeneous and large datasets collected by the authors covering different locations, spatial resolutions and platforms were used to assess the network performances in terms of transfer learning with specific regard to geographical transferability of the trained network to imagery acquired in different locations. The computational time needed to deliver these maps is also assessed. Results show that quality metrics are influenced by the composition of training samples used in the network. To promote their wider use, three pre-trained networks—optimized for satellite, airborne and UAV image spatial resolutions and viewing angles—are made freely available to the scientific community

Enhancing the role of citizen sensors in mapping: COST Action TD1202

This article introduces a strategic initiative, COST Action TD1202, focused on the role of citizen sensors in mapping. It outlines the Action's scope, aims and current status. In particular, the article outlines the potential of citizen science in mapping activities and indicates the scope of current work undertaken by the Action's four working groups. It is stressed that the Action is at an early stage and that it is open to new members

The function of remote sensing in support of environmental policy

Limited awareness of environmental remote sensing’s potential ability to support environmental policy development constrains the technology’s utilization. This paper reviews the potential of earth observation from the perspective of environmental policy. A literature review of “remote sensing and policy” revealed that while the number of publications in this field increased almost twice as rapidly as that of remote sensing literature as a whole (15.3 versus 8.8% yr−1), there is apparently little academic interest in the societal contribution of environmental remote sensing. This is because none of the more than 300 peer reviewed papers described actual policy support. This paper describes and discusses the potential, actual support, and limitations of earth observation with respect to supporting the various stages of environmental policy development. Examples are given of the use of remote sensing in problem identification and policy formulation, policy implementation, and policy control and evaluation. While initially, remote sensing contributed primarily to the identification of environmental problems and policy implementation, more recently, interest expanded to applications in policy control and evaluation. The paper concludes that the potential of earth observation to control and evaluate, and thus assess the efficiency and effectiveness of policy, offers the possibility of strengthening governance

A Proof-of-Concept of Integrating Machine Learning, Remote Sensing, and Survey Data in Evaluations: The Measurement of Disaster Resilience in the Philippines

Disaster resilience is a topic of increasing importance for policy makers in the context of climate change. However, measuring disaster resilience remains a challenge as it requires information on both the physical environment and socio-economic dimensions. In this study we developed and tested a method to use remote sensing (RS) data to construct proxy indicators of socio-economic change. We employed machine-learning algorithms to generate land-cover and land-use classifications from very high-resolution satellite imagery to appraise disaster damage and recovery processes in the Philippines following the devastation of typhoon Haiyan in November 2013. We constructed RS-based proxy indicators for N=20 barangays (villages) in the region surrounding Tacloban City in the central east of the Philippines. We then combined the RS-based proxy indicators with detailed socio-economic information collected during a rigorous-impact evaluation by DEval in 2016. Results from a statistical analysis demonstrated that fastest post-disaster recovery occurred in urban barangays that received sufficient government support (subsidies), and which had no prior disaster experience. In general, socio-demographic factors had stronger effects on the early recovery phase (0-2 years) compared to the late recovery phase (2-3 years). German development support was related to recovery performance only to some extent. Rather than providing an in-depth statistical analysis, this study is intended as a proof-of-concept. We have been able to demonstrate that high-resolution RS data and machine-learning techniques can be used within a mixed-methods design as an effective tool to evaluate disaster impacts and recovery processes. While RS data have distinct limitations (e.g., cost, labour intensity), they offer unique opportunities to objectively measure physical, and by extension socio-economic, changes over large areas and long time-scales.Zunehmende Wetterextreme und Naturkatastrophen sind Folgen des Klimawandels. Aufgrund dieser steigenden Risiken rückt die Resilienz der Bevölkerung im Katastrophenfall als zentrales Thema in den Vordergrund und hat zunehmende Bedeutung für politische Entscheidungstragende. Dennoch bleibt die Messung des mehrdimensionalen Konzepts der Katastrophenresilienz eine Herausforderung, da sie Informationen sowohl über die physische Umgebung als auch sozioökonomische Faktoren erfordert. In dieser Studie wird eine Methode entwickelt, um aus Fernerkundungsdaten (RS-Daten) Indikatoren zu entwickeln, die Aspekte des sozioökonomischen Wandels approximieren und somit messbar machen (Proxy-Indikatoren). Zu diesem Zweck wurden Algorithmen des maschinellen Lernens eingesetzt. Mit Hilfe dieser Algorithmen wurden aus hochauflösenden Satellitenbildern Klassifizierungen für Landstruktur und Landnutzung konstruiert, um Katastrophenschäden und iederaufbauprozesse auf den Philippinen nach der Zerstörung durch den Taifun Haiyan im November 2013 zu messen. Aus den RS-Daten wurden die Indikatoren für N=20 Barangays (Dörfer) in der Region um die Stadt Tacloban im zentralen Osten der Philippinen berechnet. Diese auf RS-Daten basierenden Indikatoren wurden mit detaillierten sozioökonomischen Informationen kombiniert, die für eine DEval-Evaluierung im Jahr 2016 erhoben wurden. Die Ergebnisse der statistischen Analyse zeigen, dass der schnellste Wiederaufbau nach der Katastrophe in städtischen Barangays zu beobachten war, die ausreichend staatliche Unterstützung (Subventionen) erhielten und über keine Katastrophenerfahrung verfügten. Im Vergleich hatten soziodemografische Faktoren allgemein stärkere Auswirkungen auf die frühe (0-2 Jahre) als auf die spätere (2-3 Jahre) Wiederaufbauphase. Es konnte nur ein bedingter Bezug zwischen der deutschen Entwicklungszusammenarbeit und den Wiederaufbauerfolgen festgestellt werden. Diese Studie versteht sich als Nachweis der Machbarkeit, weniger als detaillierte statistische Analyse. Sie belegt, dass hochauflösende RS-Daten und Techniken des maschinellen Lernens innerhalb eines integrierten Methodendesigns als effektives Werkzeug zur Bewertung von Katastrophenauswirkungen und Wiederherstellungsprozessen eingesetzt werden können. Trotz spezifischer Einschränkungen (hohe Kosten, Arbeitsintensität etc.) bieten RS-Daten einzigartige Möglichkeiten sowohl Umweltbedingungen als auch sozioökonomische Veränderungen über große Gebiete und lange Zeiträume hinweg objektiv messen zu können

Improving Resilience of Transport Instrastructure to Climate Change and other natural and Manmande events based on the combined use of Terrestrial and Airbone Sensors and Advanced Modelling Tools

The project PANOPTIS, funded by the European Commission under the H2020 Programme, aims at increasing the resilience of the transport infrastructures (focusing on roads) and ensuring reliable network availability under unfavourable conditions, such as extreme weather, landslides, and earthquakes. The main target is to combine downscaled climate change scenarios (applied to road infrastructures) with structural and geotechnical simulation tools and with actual data from a multi-sensor network (terrestrial and airborne-based), so as to provide the operators with an integrated tool able to support more effective management of their infrastructures at planning, maintenance and operation level. During the first stage of the project, the consortium will develop advanced technologies to monitor and control transport infrastructures, such as a Geotechnical and Structural Simulation Tool (SGSA) to predict structural and geotechnical risks in road infrastructures; drone-technologies applied to road upkeep and incident management; improved computer vision and machine learning techniques for damage diagnosis of infrastructure, and early warning systems to help operators identify and communicate emerging systemic risks. At the same time, experts in climate modelling, will analyse the possible short and long term effects of climate change on transport infrastructure (e.g. flooding, heavier snows). All the information from the different sensors, models and applications will be integrated and processed through a unique Resilience Assessment Platform that will support operators in the introduction of adaptation and mitigation strategies based on multi-risk scenarios. During the second stage of the project, ACCIONA Engineering will implement the developed technologies and methodologies in a section of the Spanish A-2 motorway, in the province of Guadalajara. PANOPTIS integrated Platform will help optimize the management and maintenance of the Ministry of Public Works' concession for a 77.5-km section, all in collaboration with ACCIONA Infrastructure Maintenance (AMISA) and ACCIONA Concessions. In parallel, PANOPTIS platform will also be implemented in a section of 62 Km of a Greek motorway, renowned for its seismic activity. The trials in Greece hosted by the operator Egnatia Odos will integrate the motorway that serves the Airport of Thessaloniki. So the scenario will integrate a modal transfer segment.Le projet PANOPTIS, financé par la Commission européenne dans le cadre du programme H2020, vise à accroître la résilience de l'infrastructure de transport et à permettre une disponibilité fiable du réseau dans des conditions défavorables, telles que les conditions météorologiques extrêmes, les glissements de terrain et les tremblements de terre. L'objectif principal doit être associé à un réseau multi-capteurs (terrestre et aéroporté) pour permettre une gestion plus efficace de leurs infrastructures au niveau de la planification, de la maintenance et de l'exploitation. Au cours de la première phase du projet, le consortium développera des technologies avancées pour surveiller et contrôler les infrastructures de transport, telles que l'outil de simulation géotechnique et structurelle (SGSA) permettant de prévoir les risques structurels et géotechniques dans les infrastructures routières; technologies de drones appliquées à l'entretien des routes et à la gestion des incidents; la vision par ordinateur et les techniques d'apprentissage automatique pour le diagnostic des infrastructures et les systèmes d'alerte précoce. Dans le même temps, des experts en modélisation du climat analyseront le potentiel du changement climatique sur les infrastructures de transport (par exemple, les inondations, les neiges plus lourdes). Toutes les informations provenant des différents capteurs, modèles et applications seront intégrées dans un scénario unique comportant plusieurs risques. Au cours de la deuxième phase du projet, ACCIONA Engineering mettra en oeuvre les technologies et les méthodologies dans une section de l'autoroute espagnole A-2, dans la province de Guadalajara. La plate-forme intégrée PANOPTIS contribuera à optimiser la gestion et la maintenance de la concession du ministère des Travaux publics pour une section de 77,5 km, le tout en collaboration avec ACCIONA Infrastructure Maintenance (AMISA) et ACCIONA Concessions. Parallèlement, la plate-forme PANOPTIS sera également mise en oeuvre dans une section de 62 Km d'une autoroute grecque réputée pour son activité sismique. Les essais en Grèce organisés par l'opérateur Egnatia Odos vont rejoindre l'aéroport de Thessalonique. Le scénario intégrera donc un segment de transfert modal

A Review

Structural disaster damage detection and characterization 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 unmanned aerial vehicles (UAVs) 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. This study provides a comprehensive review of how UAV-based damage mapping has evolved from providing simple descriptive overviews of a disaster science, to more sophisticated texture and segmentation-based approaches, and finally to studies using advanced deep learning approaches, as well as multi-temporal and multi-perspective imagery to provide comprehensive damage descriptions. The paper further reviews studies on the utility of the developed mapping strategies and image processing pipelines for first responders, focusing especially on outcomes of two recent European research projects, RECONASS (Reconstruction and Recovery Planning: Rapid and Continuously Updated Construction Damage, and Related Needs Assessment) and INACHUS (Technological and Methodological Solutions for Integrated Wide Area Situation Awareness and Survivor Localization to Support Search and Rescue Teams). Finally, recent and emerging developments are reviewed, such as recent improvements in machine learning, increasing mapping autonomy, damage mapping in interior, GPS-denied environments, the utility of UAVs for infrastructure mapping and maintenance, as well as the emergence of UAVs with robotic abilities. Document type: Articl