Panoramic Aerial Road Damage Mapping from UAV Orthomosaic Surveys
Keywords:
Infrastructure Monitoring, Unmanned Aerial Vehicles, Semantic Segmentation, Vision Transformers, Deep LearningAbstract
The rapid and accurate assessment of civil transportation infrastructure is a critical component of modern urban management and global economic stability. Traditional road surface monitoring techniques often rely on labor-intensive manual inspections or ground-based mobile mapping systems, both of which suffer from limited spatial coverage and significant operational costs. Recent advancements in unmanned aerial vehicle technology have enabled large-scale topographical data acquisition, yet standard aerial imagery frequently lacks the cohesive contextual perspective required for continuous network evaluation. This paper introduces a novel framework for continuous road damage mapping utilizing panoramic aerial orthomosaics analyzed through a sophisticated multi-scale vision transformer architecture. By leveraging the expansive field of view provided by stitched aerial panoramas, the proposed method captures extensive longitudinal infrastructure segments while maintaining the high spatial resolution necessary to identify minute structural anomalies such as micro-cracks, potholes, and surface deformations. The core contribution lies in the integration of a hierarchical transformer network that extracts features at multiple spatial resolutions, allowing the self-attention mechanisms to concurrently model localized damage characteristics and global contextual dependencies across the panoramic canvas. Extensive qualitative and quantitative analyses demonstrate that the multi-scale transformer significantly outperforms conventional convolutional neural networks in terms of segmentation precision, recall, and intersection over union. The research findings confirm that combining panoramic aerial data with advanced multi-scale attention networks provides a robust, scalable, and highly accurate solution for next-generation automated pavement management systems.
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