Abstract

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: Article

Abstract

This paper highlights the main objectives and some of the results of an extensive structural health monitoring (SHM) project begun in Khiva, Uzbekistan. The research team consists of experts from the University of California, Berkeley (Berkeley, USA) and Urgench State University (Urgench, Uzbekistan). According to archaeological data, the city of Khiva was established approximately 1500 years ago. The project started from Itchan Kala, the walled inner town of the city of Khiva. This paper describes results obtained for the Juma Mosque located inside of Itchan Kala. A continuous SHM became essential to ensure its preservation for humanity because a few structural anomalies were reported in the past. The ongoing project consists of three major phases. First, a terrestrial laser scanning (TLS) was deployed to capture and monitor anomalies of the monument’s geometry in 3D. A laser scanner was used, and the monument’s geometry was captured as a collection of points, which is called a “point cloud”. Second, there is a plan to monitor the monument by in-situ measurements conducted by accelerometers, position transducers and thermocouples in the next phase. The high-sensitivity accelerometers will be used to study the resonant frequencies due to the ambient vibrations. The position transducers and thermocouples will be used for measuring the thermal expansion and retraction of the monument’s minaret. They will also be used for monitoring the crack openings. Third, a finite-element (FE) model based on the as-found geometry of the point cloud will be generated. Its calibration will be based on the collected data to ensure acceptable correlations with the results of the conventional measurements. The FE model will be used for future restoration efforts to evaluate the performance and efficiency (if any) of the proposed restoration measures. This paper focuses on the results obtained for the Juma Mosque’s minaret. It discusses the major findings of the laser scanning project and estimates the minaret’s residual inclination and its taper. The paper shows that the minaret can be approximated by a truncated cone. A preliminary FE model of the minaret was developed, which will be updated based on the results of the upcoming ambient vibration study.