Advances in Computer Vision-Based Civil Infrastructure Inspection and Monitoring

Since its inception 30 years ago perhaps no innovation in radar technology has made such a tremendous impact on the field as that of radar interferometry. Radar interferometry uses two or more observations separated in either time or space to measure fraction of a wavelength scale range differences between the two observations. Radar interferometry is used by scientific, commercial and government institutions for numerous applications including topographic map generation, surface deformation mapping, landslide monitoring, current velocity measurement, vegetation structure determination and change detection. Radar interferometers can be flown on either spaceborne or airborne platforms or be fixed observing systems. This course is designed to provide an overview of the basic concepts of radar interferometry and an introduction to some of its applications. The course will cover basic radar imaging principles, a geometric and imaging signal perspective of the interferometric phase, interferometric correlation, basic sensitivity equations, phase unwrapping, topographic mapping and repeat pass interferometry for deformation measurements. The principles will be illustrated with examples from both spaceborne and airborne interferometric data sets. An overview of some of the major applications of radar interferometry will be presented with an emphasis on topographic and deformation mapping. The course will also briefly touch upon permanent scatter methods and polarimetric interferometry.

Radar interferometry

Natural hazards have the potential to cause catastrophic damage and significant socioeconomic loss. The actual damage and loss observed in the recent decades has shown an increasing trend. As a result, disaster managers need to take a growing responsibility to proactively protect their communities by developing efficient management strategies. A number of research studies apply artificial intelligence (AI) techniques to process disaster-related data for supporting informed disaster management. This study provides an overview of current applications of AI in disaster management during its four phases: mitigation, preparedness, response, and recovery. It presents example applications of different AI techniques and their benefits for supporting disaster management at different phases, as well as some practical AI-based decision support tools. We find that the majority of AI applications focus on the disaster response phase. This study also identifies challenges to inspire the professional community to advance AI techniques for addressing them in future research.

Applications of artificial intelligence for disaster management

Crack assessment of bridge piers using unmanned aerial vehicles (UAVs) eliminates unsafe factors of manual inspection and provides a potential way for the maintenance of transportation inf...

Image-based crack assessment of bridge piers using unmanned aerial vehicles and three-dimensional scene reconstruction

Unmanned aerial vehicles (UAV), or drones, have become popular tools for practitioners and researchers alike. Recent years have seen a significant increase in UAV uses for many applications...

Applications of UAVs in Civil Infrastructure

Unmanned aerial vehicles (UAV) are now a viable option for augmenting bridge inspections. Utilising an integrated combination of a UAV and computer vision can decrease costs, expedite inspections a...

Unmanned aerial vehicle inspection of the Placer River Trail Bridge through image-based 3D modelling

Over the last decade, unmanned aerial systems (UASs) technology has matured to the point that it is now a viable option for augmenting more traditional bridge inspection methods. Any UAS-based inspection must address questions regarding the design of the aerial platform, the choice of on-board inspection cameras, and the protocols for the human pilot to follow. The wide variety of currently available UASs and imaging systems, as well as a current lack of UAS inspection standards, can pose challenges to engineers attempting to design or test a new system. The Placer River Trail Bridge, located on the Alaskan Kenai Peninsula, has been chosen as a test site for the prototyping and testing of a new UAS bridge inspection system. The desired final inspection product guided the design and operation of the UAS. The research team emphasized a data-driven approach in developing the UAS and its associated inspection protocols. Goals included providing high-resolution scans of the bridge capable of detecting a variety of damage indicators, and producing a 3D virtual model of the bridge using Structure-from-Motion. The resulting designs of both the UAS and the inspection protocols are presented herein. The results indicate that such a data-driven approach can lead to UAS inspection systems that are more capable of meeting the needs of bridge asset managers and inspectors.

UAS-Based Inspection of the Placer River Trail Bridge: A Data-Driven Approach

The landslide complex at Gold Basin, Washington, has been drawing considerable attention after a catastrophic runout of the nearby landslide at Oso, Washington, in 2014. To evaluate potential threats of the Gold Basin landslide to the campground down the slope, remote sensing and numerical modeling were integrated to monitor recent landslide activity and simulate hypothetical runout scenarios. Bare-earth LiDAR DEM (digital elevation model) differencing, InSAR (Interferometric Synthetic Aperture Radar), and offset tracking of SAR images reveal that localized collapses at the headscarps have been the primary type of landslide activity at Gold Basin from 2005 to 2019, and currently no signs indicative of movement of a large centralized block or a deep-seated main body were detected. The maximum horizontal deformation rate is 5 m/year occurring primarily from headscarp recession of the middle lobe, and the annual landsliding volume of the whole landslide complex averages 1.03 × 105 m3. From three-dimensional limit equilibrium analysis of generalized terrace structures, the maximum landslide volume is estimated as 2.0 × 106 m3. Simulations of hypothetical runout scenarios were carried out using the depth-averaged two-phase model D-claw with above-obtained landslide geometry constraints. The simulation results demonstrate that debris flows with volume less than 105 m3 only pose limited threats to the campground, while volumes over 106 m3 could cause severe damages. Consequently, the estimated maximum landslide volume of 2.0 × 106 m3 suggests a potential risk to the campground nearby. Adaption of our methodology could prove useful for evaluating other similar landslides globally for hazards prevention and mitigation.

Landslide monitoring and runout hazard assessment by integrating multi-source remote sensing and numerical models: an application to the Gold Basin landslide complex, northern Washington

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Unmanned aerial vehicle inspection of the Placer River Trail Bridge through image-based 3D modelling

UAS-Based Inspection of the Placer River Trail Bridge: A Data-Driven Approach

Landslide monitoring and runout hazard assessment by integrating multi-source remote sensing and numerical models: an application to the Gold Basin landslide complex, northern Washington