Abstract
Abstract Automatic identification of (paved) roads is crucial for applications such as autonomous navigation and urban planning. We use high-resolution aerial images, airborne Lidar, and low-resolution multi-spectral images to perform pixel-wise segmentation of road pixels. We utilize multi-modal data along with binary and multi-class ground truth information to train convolutional neural networks (CNNs) and modify an existing CNN architecture to enable training on the lower resolution multi-spectral images. We propose an ensemble of CNNs, called QuadRoad, which combines the results of four trained CNN models to boost the binary road segmentation accuracy. QuadRoad achieves higher accuracy than existing road segmentation techniques.
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