Advertisement

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)

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.

Keywords

Deep learning Convolutional neural networks Lidar Remote sensing 

Notes

Acknowledgments

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.

References

  1. 1.
    Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: a deep convolutional encoder-decoder architecture for image segmentation (2015). arXiv preprint arXiv:1511.00561
  2. 2.
    BenTaieb, A., Hamarneh, G.: Topology aware fully convolutional networks for histology gland segmentation. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 460–468. Springer, Cham (2016). doi: 10.1007/978-3-319-46723-8_53 CrossRefGoogle Scholar
  3. 3.
    Camp-Valls, G., Bruzzone, L.: Kernel Methods for Remote Sensing Data Analysis/Edited by Gustavo Camps-Valls Lorenzo Bruzzone. Wiley, Chichester (2009)CrossRefGoogle Scholar
  4. 4.
    Douglas, D.H., Peucker, T.K.: Algorithms for the reduction of the number of points required to represent a digitized line or its caricature. Cartographica: Int. J. Geograph. Inf. Geovisualization 10(2), 112–122 (1973)CrossRefGoogle Scholar
  5. 5.
    Eigen, D., Fergus, R.: Predicting depth, surface normals and semantic labels with a common multi-scale convolutional architecture. In: Proceedings of IEEE International Conference on Computer Vision, pp. 2650–2658 (2015)Google Scholar
  6. 6.
    Farabet, C., Couprie, C., Najman, L., LeCun, Y.: Learning hierarchical features for scene labeling. IEEE Trans. Pattern Anal. Machine Intell. 35(8), 1915–1929 (2013)CrossRefGoogle Scholar
  7. 7.
    Ferraz, A., Mallet, C., Chehata, N.: Large-scale road detection in forested mountainous areas using airborne topographic lidar data. ISPRS J. Photogramm. Remote Sens. 112, 23–36 (2016)CrossRefGoogle Scholar
  8. 8.
    Hariharan, B., Arbeláez, P., Girshick, R., Malik, J.: Hypercolumns for object segmentation and fine-grained localization. In: Proceedings of IEEE Conference Computer Vision Pattern Recognition, pp. 447–456 (2015)Google Scholar
  9. 9.
    He, K., Zhang, X., Ren, S., Sun, J.: Delving deep into rectifiers: surpassing human-level performance on imagenet classification. In: Proceedings of IEEE International Conference on Computer Vision, pp. 1026–1034 (2015)Google Scholar
  10. 10.
    Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift (2015). arXiv preprint arXiv:1502.03167
  11. 11.
    ISPRS: ISPRS 2D Semantic Labeling Contest (2015). http://www2.isprs.org/commissions/comm3/wg4/semantic-labeling.html
  12. 12.
    Kampffmeyer, M., Salberg, A.B., Jenssen, R.: Semantic segmentation of small objects and modeling of uncertainty in Urban remote sensing images using deep convolutional neural networks. In: Proceedings of IEEE Conference on Computer Vision Pattern Recognition Workshops, pp. 1–9 (2016)Google Scholar
  13. 13.
    Lagrange, A., Saux, B.L., Beaupère, A., Boulch, A., Chan-Hon-Tong, A., Herbin, S., Randrianarivo, H., Ferecatu, M.: Benchmarking classification of earth-observation data: from learning explicit features to convolutional networks. In: 2015 IEEE International Geoscience Remote Sensing Symposium (IGARSS), pp. 4173–4176 (2015)Google Scholar
  14. 14.
    Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of IEEE Conference on Computer Vision Pattern Recognition, pp. 3431–3440 (2015)Google Scholar
  15. 15.
    Mnih, V., Hinton, G.E.: Learning to detect roads in high-resolution aerial images. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6316, pp. 210–223. Springer, Heidelberg (2010). doi: 10.1007/978-3-642-15567-3_16 CrossRefGoogle Scholar
  16. 16.
    Paisitkriangkrai, S., Sherrah, J., Janney, P., Hengel, A.: Effective semantic pixel labelling with convolutional networks and conditional random fields. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 36–43 (2015)Google Scholar
  17. 17.
    Penatti, O.A.B., Nogueira, K., dos Santos, J.A.: Do deep features generalize from everyday objects to remote sensing and aerial scenes domains? In: Proceedings of IEEE Conference on Computer Vision Pattern Recognition Workshops, pp. 44–51 (2015)Google Scholar
  18. 18.
    Pinheiro, P., Collobert, R.: Recurrent convolutional neural networks for scene parsing (2013). arXiv preprint arxiv:1306.2795
  19. 19.
    Salberg, A.B.: Detection of seals in remote sensing images using features extracted from deep convolutional neural networks. In: 2015 IEEE International Geoscience Remote Sensing Symposium (IGARSS), pp. 1893–1896 (2015)Google Scholar
  20. 20.
    Sermanet, P., Eigen, D., Zhang, X., Mathieu, M., Fergus, R., LeCun, Y.: OverFeat: integrated recognition, localization and detection using convolutional neural networks. In: International Conference on Learning Representations (ICLR), CBLS, Banff, Canada, April 2014Google Scholar
  21. 21.
    Trier, Ø.D.: Evaluation of methods for detection of roads in laser data - preliminary results. LasTrak pilot project (in Norwegian). NR-Note SAMBA/09/15, Norwegian Computing Center, Oslo (2015)Google Scholar
  22. 22.
    Wang, W., Yang, N., Zhang, Y., Wang, F., Cao, T., Eklund, P.: A review of road extraction from remote sensing images. J. Traffic Transp. Eng. (Engl. Ed.) 3(3), 271–282 (2016)CrossRefGoogle Scholar

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

Personalised recommendations