Using the U‐net convolutional network to map forest types and disturbance in the Atlantic rainforest with very high resolution images

Wagner, FH, Sanchez, A, Tarabalka, Y et al. (6 more authors) (2019) Using the U‐net convolutional network to map forest types and disturbance in the Atlantic rainforest with very high resolution images. Remote Sensing in Ecology and Conservation, 5 (4). pp. 360-375. ISSN 2056-3485

Abstract

Metadata

Authors/Creators:
Copyright, Publisher and Additional Information: © 2019 The Authors. Remote Sensing in Ecology and Conservation published by John Wiley & Sons Ltd on behalf of Zoological Society of London. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
Keywords: Deep learning; Image segmentation; Keras; Rstudio; Tensorflow; Tree crown delineation; Tree species detection; Vegetation type detection; WorldView‐3 image
Dates:
  • Published: December 2019
  • Accepted: 1 February 2019
  • Published (online): 5 March 2019
Institution: The University of Leeds
Academic Units: The University of Leeds > Faculty of Environment (Leeds) > School of Geography (Leeds) > Ecology & Global Change (Leeds)
Funding Information:
FunderGrant number
EU - European Union291585 (ERC 2011 ADG)
Royal SocietyNo External Ref
NERCNE/K01644X/1
NERCNE/N012542/1
Depositing User: Symplectic Publications
Date Deposited: 14 Jun 2019 12:41
Last Modified: 30 Jan 2020 16:45
Status: Published
Publisher: Wiley Open Access
Identification Number: https://doi.org/10.1002/rse2.111

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