Unveiling the Road Matrix: Development of Versatile Pavement Surface Models from Low-resolution Aerial Images

The highly accurate retrieval of large-scale road surfaces is crucial when developing nationwide digital twins. Many existing approaches have focused on detecting road surfaces from high-resolution aerial images, videos captured by dashcams, or digital surface models (DSMs), either individually or in combination with multiple data sources for stronger guarantees. The results of these implementations are highly dependent on the quality of the original data sources that are selected. Additionally, such approaches primarily employ deep learning, which is associated with challenges such as inefficient training and poor generalization of model results. Therefore, in this study, we propose a method for developing wide-ranging pavement surface models by realizing an accurate three-dimensional reconstruction module based on images with a lower resolution and introduce a multilevel Markov random field (MRF) model for mapping road networks from reconstructed sparse point cloud scenes. The experimental results demonstrated that the proposed development pipeline achieved an average of state-of-the-art performance on the International Society for Photogrammetry and Remote Sensing (ISPRS) dense matching dataset with a satisfactory root mean square error (RMSE) and obtained an overall accuracy (OA) of 90.3% and a mean intersection over union (mIoU) of 82.3% in the segmentation task. Using the Geospatial Information Authority of Japan (GSI) aerial image dataset, the robustness of the method in developing road surface models over a wide range of scenarios was further proven while accommodating less stricter source data requirements.