Artificial Intelligence (AI) is swiftly reshaping our understanding of the world. Leveraging AI for environmental, ecological, and meteorological analysis, as well as for evaluating natural resources, demands innovative machine learning techniques tailored to geospatial data. Such techniques need to capture spatiotemporal (ST) relationships, scale computationally with increasing data volumes, deliver precise predictions along with uncertainty estimates, handle non-Euclidean geometries, and effectively combine information from diverse sources.

SLIMNETS will investigate novel approaches for modeling spatiotemporal data inspired by statistical field theory concepts. It will employ Boltzmann-Gibbs processes to design sparse local interaction models and apply “effective medium” principles to create efficient active regression algorithms for Gaussian processes.

SLIMNETS will develop multi-output kernels grounded in vector Boltzmann-Gibbs processes to address existing challenges, create multivariate spatiotemporal models with sparse equilibrium operators, and build regression networks featuring localized sparse interactions. These innovations will deliver a scalable framework for spatiotemporal interpolation, forecasting, and simulation, surpassing the performance of conventional Gaussian process models.

SLIMNETS also seeks to develop computationally efficient algorithms for spatiotemporal data, extend the adaptability of Gaussian processes through physically inspired multivariate kernels, and design regression models grounded in generalized notions of spatiotemporal distance. These advances will enable AI-driven solutions for environmental monitoring, early warning systems, automated pollution detection, high-resolution soil mapping, and the reconstruction of missing data in remote sensing products.