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\begin{document}

\title{The detection of socio-economic impacts of protected area creation}



\author[1,1]{Alison Specht}%
\author[2,2]{M. Jeaneth Machicao Justo}%
\author[3,3]{Pedro Corrêa}%
\author[4,4]{Rodolphe Devillers}%
\author[5,5]{Yasuhisa Kondo}%
\author[6,6]{David Mouillot}%
\author[7,7]{Yasuhiro Murayama}%
\author[8,8]{Shelley Stall}%
\author[9,9]{E. Jamie Trammell}%
\author[2,2]{Danton Ferreira Vellenich}%
\affil[1]{Terrestrial Ecosystem Research Network}%
\affil[2]{Universidade de São Paulo Escola Politécnica}%
\affil[3]{Universidade de São Paulo Escola Politécnica,Escola Politécnica da Universidade de São Paulo}%
\affil[4]{Institut de Recherche pour le Développement}%
\affil[5]{Research Institute for Humanity and Nature}%
\affil[6]{University of Montpellier, France}%
\affil[7]{National Institute of Information and Communications Technology}%
\affil[8]{American Geophysical Union}%
\affil[9]{Southern Oregon University,University of Alaska Anchorage}%


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  \date{May 27, 2024}


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\begin{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.%
\end{abstract}%



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