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

  • Arnt-Børre Salberg
  • Øivind Due Trier
  • Michael Kampffmeyer
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10270)


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.


Deep learning Convolutional neural networks Lidar Remote sensing 



Part of this research was financed by the Norwegian Mapping Authority, Hamar regional office, which also provided vector data. Airborne laser scanning data was provided by Oppland County Administration.


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Arnt-Børre Salberg
    • 1
  • Øivind Due Trier
    • 1
  • Michael Kampffmeyer
    • 2
  1. 1.Norwegian Computing CenterOsloNorway
  2. 2.UiT - The Arctic University of NorwayTromsøNorway

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