It would seem that former Master student Laurens Arp will not be leaving the ADA group just yet, as he has just renewed his membership for the group, this time as a PhD student.
Prior to starting his PhD studies, Laurens worked on his Master’s Thesis on spatial interpolation within the ADA group supervised by dr. Mitra Baratchi and prof.dr. Holger Hoos, culminating in his MSc degree obtained in 2020.
The research Laurens will be carrying out now is part of dr. Mitra Baratchi’s “Physics-aware Spatio-temporal Machine Learning for Earth Observation Data” project, which involves a collaboration with the European Space Agency. In this project, the goal is to estimate environmental parameters (leaf density, CO2 levels in the atmosphere, etc.) from optical Earth observation data acquired by satellites. Generally speaking, there are two approaches one might take to this end. The first of those, traditionally favoured by the ecology community, would be to create inverted versions of deterministic radiative transfer models, which model how radiation (captured in Earth observation data) is affected by environmental parameters (ground truth). The other approach, perhaps more familiar to the AI and machine learning communities, would be to use spatio-temporal machine learning methods (e.g., temporal CNNs) to model the correlation between data (spectral images of radiation) and ground truth (environmental parameters). Both approaches have their own strengths and weaknesses, which motivates the development of hybrid models benefiting from the strengths of both. Thus, the creation of such hybrid models is what the project will aim to achieve.
Between global warming, the loss of biodiversity through mass extinction and other ecological and environmental perils the Earth is faced with these days, the challenges we, as residents of the Earth, are faced with can be daunting indeed. It is our hope that the models we will create will provide a meaningful contribution to the global efforts to address these issues.