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The detection of socio-economic impacts of protected area creation
  • +7
  • Alison Specht,
  • M. Jeaneth Machicao Justo,
  • Pedro Corrêa,
  • Rodolphe Devillers,
  • Yasuhisa Kondo,
  • David Mouillot,
  • Yasuhiro Murayama,
  • Shelley Stall,
  • E. Jamie Trammell,
  • Danton Ferreira Vellenich
Alison Specht
Terrestrial Ecosystem Research Network, Terrestrial Ecosystem Research Network

Corresponding Author:[email protected]

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M. Jeaneth Machicao Justo
Universidade de São Paulo Escola Politécnica, Universidade de São Paulo Escola Politécnica
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Pedro Corrêa
Universidade de São Paulo Escola Politécnica,Escola Politécnica da Universidade de São Paulo, Universidade de São Paulo Escola Politécnica,Escola Politécnica da Universidade de São Paulo
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Rodolphe Devillers
Institut de Recherche pour le Développement, Institut de Recherche pour le Développement
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Yasuhisa Kondo
Research Institute for Humanity and Nature, Research Institute for Humanity and Nature
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David Mouillot
University of Montpellier, France, University of Montpellier, France
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Yasuhiro Murayama
National Institute of Information and Communications Technology, National Institute of Information and Communications Technology
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Shelley Stall
American Geophysical Union, American Geophysical Union
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E. Jamie Trammell
Southern Oregon University,University of Alaska Anchorage, Southern Oregon University,University of Alaska Anchorage
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Danton Ferreira Vellenich
Universidade de São Paulo Escola Politécnica, Universidade de São Paulo Escola Politécnica
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Abstract

This paper discusses a project aimed at detecting whether protected areas (PAs) influence the socioeconomic well-being of adjacent communities. The Belmont Forum funded PARSEC project is using satellite images and deep learning algorithms to predict socioeconomic conditions. In this paper we show our ongoing work for the selection of PAs, development of methodology using deep learning to detect socioeconomic indicators from remote sources, facilitation in data management and the approach used to handle a complex inter-disciplinary and trans-national team. We note the challenges in selecting case studies with examples from Australia, Brazil, Japan and the USA, and meshing remote sensed data with census data. We discuss the advantages of good data management for the individual and for the project and some simple steps to make this easy.