Geospatial information technologies for resilient and sustainable society (GeoAI)

The research project "Geospatial Information Technologies for a Resilient and Sustainable Society (GeoAI)" addresses the scientific challenges posed by the extremely rapid development of geospatial technologies and geospatial science. High-quality spatial data or information and spatio-temporal models of spatial phenomena are crucial for addressing past, current, and future societal challenges, as most decisions − whether strategic or real-time − are location-based.

The project focuses on scientific challenges in the new scientific field of geospatial artificial intelligence, which builds on innovations in geospatial information science combined with advanced artificial intelligence (AI) methods and the ability to process large amounts of data. This is an ambitious initiative that aims to surpass previous scientific and research achievements in the field of geospatial information science. To this end, we have formed a consortium of leading researchers and research institutions from Slovenia in the fields of geoinformatics and GIS, data science, and artificial intelligence, together with institutions and researchers in selected application areas, namely geology, geography, hydrography, spatial planning, and archaeology.

The central theme of the research project is spatial modeling in support of built and natural environment management, with a particular focus on planning measures to increase society's resilience to climate change and related natural disasters. In doing so, we focus primarily on the challenges posed by new geospatial technologies for the mass capture of spatial and space-related data, and on innovative approaches to processing large amounts of data, including geospatial artificial intelligence and machine learning.

Within the framework of the project, we will improve existing approaches and develop new ones for the automatic recognition and mapping of phenomena in space, with an emphasis on spatial modeling with high spatial and temporal resolution. The latter is crucial for recognizing changes in space and for high-quality spatial-temporal modeling of phenomena, which we also include in the project activities through selected use cases.

Five organisations and 66 researchers are participating in the project.