The aim of this PhD project is to develop a cloud based artificial neural network for processing large remotely sensed data sets in order to generate essential biodiversity variables (as defined by Pereira et al. (2013) and Skidmore et al. (2015)). The PhD candidate, in combination with supervisors and programming support, will develop innovative artificial intelligence techniques for estimating biodiversity variables using massive cloud based data sets of satellite remotely sensed, in situ and ancillary data. Potential candidate biodiversity variables to be retrieved from satellite remote sensing include pertinent indicators of ecosystem function, ecosystem structure and species traits. The research will result in a PhD thesis.