Earth Observation with Optimal Transport for Artificial Intelligence – OTTOPIA

Earth Observation, whether it be by satellites, airborne captors or drones, allows a better understanding of the dynamics of environmental systems or our human society. It is a decisive tool to measure the impact of mankind on earth. In the last 50 years, the fast development of spatial missions and of the technology of the associated captors yields an unprecedented amount of data, largely under-exploited. Artificial intelligence can become a major help toward exploiting this wealth of information, by automatizing tasks cantoned to human operators, or even combining them to produce novel knowledges. Yet, the earth observation data come with specific challenges not only related to their volume but also their complexity.
The OTTOPIA Chair project proposes to tackle some of them through the prism of Optimal Transport theory applied to machine learning.
This mathematical tool makes it possible to apprehend the data through their distributions, and no longer as a sum of distinct individuals. Following significant advances in computational aspects, it has recently emerged as a tool of choice for multiple learning problems. We propose to exploit its principles on four challenges: 1. multi-modality and considering the heterogeneity of the data at transfer of learning, 2. Learning with few data, possibly corrupted by label noise, 3. Security of AI algorithms in Earth observation; and 4. Visual Question Answering, i.e. interacting with remote sensing data through natural language questions. The contributions of the Chair will naturally aim at fundamental developments in AI but also new applied methodologies for which a strong industrial transfer potential is envisaged. The teaching project of the Chair is linked among other things to the courses in data science of the University of Bretagne Sud (UBS), as well as to a new Erasmus Mundus Master's degree on the analysis of data from the observation of the earth (Geodata science). The incumbent identified for this chair is Nicolas COURTY, University Full Professor at UBS and member of IRISA. He is a specialist in optimal transport, with a solid experience in machine learning and remote sensing, and regularly publishes in leading AI conferences (NIPS, ICML, ICLR, etc.). 3 non-academic partners are planned in the chair: CNES (French space agency) and 2 start-ups (WIPSEA and Picterra). They contribute to the chair project by providing data, use cases and expertise.