Road Segmentation

Road segmentation, the task of automatically identifying road areas in images or point clouds from various sources like satellite imagery, aerial photographs, and LiDAR scans, aims to create accurate and efficient maps for applications such as autonomous driving and urban planning. Current research emphasizes developing robust and computationally efficient models, often employing convolutional neural networks (CNNs) like U-Net and its variants, incorporating multi-modal data fusion (combining image, LiDAR, and GPS data), and addressing challenges like handling occlusions and variations in road appearance across different geographical locations and weather conditions. These advancements are crucial for improving the reliability and safety of autonomous systems and enabling more effective infrastructure management and urban development.