Application of Fully Convolutional Neural Networks to Mapping Industrial Oil Palm Plantations

  • Artem BaklanovEmail author
  • Michael Khachay
  • Maxim Pasynkov
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11179)


This research is motivated by sustainability problems of oil palm expansion. Fast-growing industrial Oil Palm Plantations (OPPs) in the tropical belt of Africa, Southeast Asia and parts of Brazil lead to significant loss of rainforest and contribute to the global warming by the corresponding decrease of carbon dioxide absorption. We propose a novel approach to monitoring of the expansion of OPPs based on an application of state-of-the-art Fully Convolutional Neural Networks (FCNs) to solve Semantic Segmentation Problem for Landsat imagery. The proposed approach significantly outperforms per-pixel classification methods based on Random Forest using texture features, NDVI, and all Landsat bands. Moreover, the trained FCN is robust to spatial and temporal shifts of input data. The paper provides a proof of concept that FCNs as semi-automated methods enable OPPs mapping of entire countries and may serve for yearly detection of oil palm expansion.


Mapping tree plantations Remote sensing Semi-automated methods FCN-8s 


  1. 1.
  2. 2.
    Austin, K., Mosnier, A., Pirker, J., McCallum, I., Fritz, S., Kasibhatla, P.: Shifting patterns of oil palm driven deforestation in Indonesia and implications for zero-deforestation commitments. Land Use Policy 69, 41–48 (2017). Scholar
  3. 3.
    Belgiu, M., Drãguţ, L.: Random forest in remote sensing: a review of applications and future directions. ISPRS J. Photogram. Remote Sens. 114, 24–31 (2016). Scholar
  4. 4.
    Carlson, K.M., Curran, L.M., Asner, G.P., Pittman, A.M., Trigg, S.N., Marion Adeney, J.: Carbon emissions from forest conversion by Kalimantan oil palm plantations. Nat. Clim. Change 3, 283–287 (2013). Scholar
  5. 5.
    Chen, L., Kokkinos, I., Murphy, K., Yuille, A.: DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Trans. Pattern Anal. Mach. Intell. 40, 834–848 (2018). Scholar
  6. 6.
    Chong, K.L., Kanniah, K.D., Pohl, C., Tan, K.P.: A review of remote sensing applications for oil palm studies. Geo-spatial Inf. Sci. 20(2), 184–200 (2017). Scholar
  7. 7.
    Conners, R.W., Trivedi, M.M., Harlow, C.A.: Segmentation of a high-resolution urban scene using texture operators. Comput. Vis. Graph. Image Process. 25(3), 273–310 (1984). Scholar
  8. 8.
    Daliman, S., Rahman, S., Bakar, S., Busu, I.: Segmentation of oil palm area based on GLCM-SVM and NDVI. In: IEEE Region 10 Symposium, pp. 645–650 (2014).
  9. 9.
    Fu, G., Liu, C., Zhou, R., Sun, T., Zhang, Q.: Classification for high resolution remote sensing imagery using a fully convolutional network. Remote Sens. 9(5), 498 (2017). Scholar
  10. 10.
    Garcia-Garcia, A., Orts-Escolano, S., Oprea, S., Villena-Martinez, V., Jose Garcia-Rodriguez, V.: A review on deep learning techniques applied to semantic segmentation. Manuscript 1 (2017)Google Scholar
  11. 11.
    Gaveau, D.L.A., et al.: Rapid conversions and avoided deforestation: examining four decades of industrial plantation expansion in Borneo. Sci. Rep. 6 (2016).
  12. 12.
    Glorot, X., Bengio, Y.: Understanding the difficulty of training deep feedforward neural networks. In: Teh, Y.W., Titterington, M. (eds.) Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, Proceedings of Machine Learning Research, vol. 9, pp. 249–256. PMLR, Sardinia, 13–15 May 2010.
  13. 13.
    Gutiérrez-Vélez, V.H., DeFries, R.: Annual multi-resolution detection of land cover conversion to oil palm in the Peruvian Amazon. Remote Sens. Environ. 129, 154–167 (2013). Scholar
  14. 14.
    Hansen, M.C., et al.: High-resolution global maps of 21st-century forest cover change. Science 342(6160), 850–853 (2013). Scholar
  15. 15.
    Haralick, R., Shanmugam, K., Dinstein, I.: Textural features for image classification. IEEE Trans. Syst. Man Cybern. SMC–3, 610–621 (1973). Scholar
  16. 16.
    Huang, Z., Pan, Z., Lei, B.: Transfer learning with deep convolutional neural network for SAR target classification with limited labeled data. Remote Sens. 9(9), 907 (2017). Scholar
  17. 17.
    Kamiran, N., Sarker, M.L.R.: Exploring the potential of high resolution remote sensing data for mapping vegetation and the age groups of oil palm plantation. IOP Conf. Ser.: Earth Environ. Sci. 18(1), 012181 (2014). Scholar
  18. 18.
    Lee, J.S.H., Wich, S., Widayati, A., Koh, L.P.: Detecting industrial oil palm plantations on Landsat images with Google Earth Engine. Remote Sens. Appl. Soc. Environ. 4, 219–224 (2016). Scholar
  19. 19.
    Lu, D., Weng, Q.: A survey of image classification methods and techniques for improving classification performance. Int. J. Remote Sens. 28(5), 823–870 (2007). Scholar
  20. 20.
    Mucherino, A., Papajorgji, P.J., Pardalos, P.M.: Data Mining in Agriculture, 1st edn. Springer, New York (2009). Scholar
  21. 21.
    Nooni, I., Duker, A., Van Duren, I., Addae-Wireko, L., Osei Jnr, E.: Support vector machine to map oil palm in a heterogeneous environment. Int. J. Remote Sens. 35(13), 4778–4794 (2014). Scholar
  22. 22.
    Petersen, R., et al.: Mapping tree plantations with multispectral imagery: preliminary results for seven tropical countries. Technical report, World Resources Institute (2016).
  23. 23.
    Shelhamer, E., Long, J., Darrell, T.: Fully convolutional networks for semantic segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39, 640–651 (2017). Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.National Research University Higher School of EconomicsSt. PetersburgRussia
  2. 2.International Institute for Applied Systems AnalysisLaxenburgAustria
  3. 3.Krasovsky Institute of Mathematics and MechanicsYekaterinburgRussia
  4. 4.Ural Federal UniversityYekaterinburgRussia

Personalised recommendations