This paper describes the methods that we have implemented in the context of the GeoLifeCLEF 2020 machine learning challenge. The goal of this challenge is to advance the state-of-the-art in location- based species recommendation on a very large dataset of 1.9 million species observations, paired with high-resolution remote sensing imagery, land cover data, and altitude. We provide a detailed description of the algorithms and methodology, developed by the LIRMM / Inria team, in order to facilitate the understanding and reproducibility of the obtained results.