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The focus of this study is to detect sinkhole hazards using airborne light detection and ranging (LiDAR) data. The premise is sinkholes, particularly those close to transportation infrastructure assets, could cause substantial damages to infrastructure assets, and therefore, being able to accurately and rapidly detect them is essential. However, it is expensive, time-consuming, labor-intensive, and unsafe to survey sinkholes using conventional ground observation methods. This research project was focused on developing accurate and rapid airborne LiDAR-based sinkhole detection and mapping methods, and transfer the technologies to transportation engineers for implementation and workforce development. The project team also identified best practices for implementation of a state-level sinkhole hazard management system (SHMS). In addition, a guidebook was developed for airborne LiDAR-based sinkhole detection and mapping for professional education and training.

The effectiveness of LiDAR to detect existing sinkholes has received very limited attention. Most of the research on LiDAR-based sinkhole detection postulates that morphological-based surface feature extraction methods can effectively detect sinkholes because of their geometric properties – sinkholes are oval-shaped concave depressions in the Earth’s surface. However, sinkholes have varying sizes, shapes, and appearance given various landforms, which adds even greater challenges to further improving the detection accuracy of methods that are based solely on morphology; for example, a dry stock pond may be incorrectly detected as a sinkhole. The proposed research used airborne LiDAR data in combination with auxiliary context such as site and association to improve the accuracy of the morphological-based sinkhole detection methods, and implement these by developing tools that can be used in standard geographic information systems (GIS). This methodology allows for the development of a robust LiDAR-based sinkhole detection toolset that provides an adequate degree of accuracy while maximizing the ability to assist inspectors with varying expertise.


Tran-SET Project No. 18GTUNM01