Fully Convolutional Neural Networks for Mapping Oil Palm Plantations in Kalimantan

  • Artem BaklanovEmail author
  • Michael Khachay
  • Maxim Pasynkov
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11353)


This research is motivated by the global warming problem, which is likely influenced by human activity. Fast-growing oil palm plantations 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 development of such plantations based on an application of state-of-the-art Fully Convolutional Neural Networks (FCNs) to solve Semantic Segmentation Problem for Landsat imagery.


Remote sensing Mapping Landsat Fully convolutional neural network 


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.National Research University Higher School of EconomicsSt. PetersburgRussia
  2. 2.International Institute for Applied Systems Analysis (IIASA)LaxenburgAustria
  3. 3.Krasovsky Institute of Mathematics and MechanicsEkaterinburgRussia
  4. 4.Ural Federal UniversityEkaterinburgRussia

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