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Road Extraction and Road Width Estimation Via Fusion of Aerial Optical Imagery, Geospatial Data, and Street-Level Images

Road information extraction based purely on remote sensing can be affected by occlusions of the road surface caused by trees, shadows, and buildings. We propose a multimodal fusion method that addresses road extraction and road width estimation by combining aerial imagery, monocular images taken at ground level (street-level), and geospatial data (Open-StreetMap). The method combines semantic segmentation through convolutional neural networks, Voronoi diagram processing, and graph matching.