Large-Scale Mapping of Small Roads in Lidar Images Using Deep Convolutional Neural Networks

Abstract

Detailed and complete mapping of forest roads is important for the forest industry since they are used for timber transport by trucks with long trailers. This paper proposes a new automatic method for large-scale mapping forest roads from airborne laser scanning data. The method is based on a fully convolutional neural network that performs end-to-end segmentation. To train the network, a large set of image patches with corresponding road label information are applied. The final network is then applied to detect and map forest roads from lidar data covering the Etnedal municipality in Norway. The results show that we are able to map the forest roads with an overall accuracy of 97.2%. We conclude that the method has a strong potential for large-scale operational mapping of forest roads.