Natural hazards pose a significant risk to transport infrastructure and can cause annual direct damage of 3.1 to 22 billion US dollars globally, with 84% of it being flooding-related. Cost-effective approaches to assessing road damage and conditions are vital for repairing and reconstructing the transportation infrastructure after hazards. We conducted a study that presents a novel methodology developed for generating highly detailed orthomosaics of road surfaces, achieving millimeter-level spatial resolution. The approach utilizes panoramic photos obtained from a mobile camera system, coupled with Structure-from-Motion (SfM) technology. A key aspect of the methodology is the accurate masking of the ego-vehicle, sky, and moving objects (such as vehicles, bicycles, and pedestrians) present in the street scenes captured by the photos. This masking process involves a combination of deep learning algorithms, image processing techniques, and manual editing. The study demonstrates that removing these objects from the images significantly improves photo alignment precision and enhances the overall quality of the orthomosaics. The resulting orthomosaics are found to be highly applicable for GIS analysis and the assessment of road conditions and damages.

The 360-degree panoramic photos were captured using an NCTECH iStar Pulsar mobile mapping system provided by NDPTC.  A total of 102, 272, and 100 panoramic photos were used in Kuhio, Ala Manoa, and UH, respectively, for our data analysis. Orthomosaics were generated from these photos using the methodology developed in this study and the Structure-from-Motion technique.