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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)

Abstract

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.

Keywords

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

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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

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