Recovery of urban 3D road boundary via multi-source data.

The mapping of road boundaries provides critical information about roads for urban road traffic safety. This paper presents a deep learning-based framework for recovering 3D road boundary using multi-source data, which include mobile laser scanning (MLS) point clouds, spatial trajectory data, and remote sensing images. The proposed road recovery method uses extracted 3D road boundaries from MLS point clouds as inputs. First, after automatic erroneous boundary removal, a CNN-based boundary completion model completes road boundaries. Then, to refine the imperfect road boundaries, road centerlines generated from dynamic taxi GPS trajectory data and remote sensing images are used as completion guidance for a generative adversarial nets model to obtain more accurate and complete road boundaries. Finally, after associating a sequence of taxi GPS recorded trajectory points with the correct 3D road boundaries, inherent geometric road characteristics and road dynamic information are extracted from the complete boundaries and taxi GPS trajectory data, respectively. The testing dataset contains two urban road MLS datasets, and the KITTI dataset. The experimental results on point clouds from different sensors demonstrate our proposed method is effective and promising for recovering 3D road boundary and extracting road characteristics. [ABSTRACT FROM AUTHOR]