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.