Artificial intelligence-empowered point cloud technologies for rapid resilience assessment of urban infrastructure under environment extremes: A comprehensive review

Natural disasters such as earthquakes, hurricanes, and floods present significant challenges to the inspection and assessment of damaged urban infrastructure, often hindering rapid emergency response and recovery. Recent advances in point cloud technology, especially when combined with artificial intelligence, are transforming post-disaster damage assessment by enabling fast, high-resolution, and multi-dimensional data acquisition across complex urban environments. This review systematically categorizes and synthesizes current research on the use of Artificial Intelligence(AI)-empowered point cloud technologies for resilience assessment of urban infrastructure under environmental extremes. The approaches are organized according to data availability, including methods that compare pre- and post-disaster point cloud data as well as those that rely solely on post-disaster data for rapid damage evaluation. The review further extends to the assessment of a wide range of civil infrastructure, such as bridges, roads, utility networks, and lifeline systems, highlighting how different platforms and sensor types are selected based on specific disaster scenarios and assessment objectives. For each category of method and infrastructure type, the advantages, limitations, and distinctive features of point cloud-based assessment techniques are thoroughly analyzed. Finally, the paper discusses current challenges and emerging trends in intelligent, automated, and multi-source point cloud solutions, and outlines key directions for future research to enhance the resilience and sustainability of cities facing environmental extremes.