About the project

Geospatial Information Technologies for a Resilient and Sustainable Society

The GeoAI project develops advanced geospatial modeling and analytics methods to support the sustainable management of the built and natural environment.

Basic Project Information

Duration: 36 months Jan 1, 7. 2025 – Dec 30, 6. 2028
Code: GC-0006
Lead Partner: University of Ljubljana, Faculty of Civil and Geodetic Engineering (UL FGG)
Project Manager: Prof. Dr. Anka Lisec
Other Partner Organizations: University of Ljubljana, Faculty of Computer and Information Science (UL FRI); Jožef Stefan Institute (IJS); Research Centre of the Slovenian Academy of Sciences and Arts (ZRC SAZU); Geological Survey of Slovenia (GeoZS)
Funding Source: ARIS – Slovenian Research and Innovation Agency
Keywords: GIS, Earth observation, spatial data, spatial information, satellite data, UAV, LiDAR, raster data, point cloud, 3D data model, CityGML, time series, artificial intelligence, machine learning, deep learning, semantics, spatial analytics, spatial planning, natural disasters

Consortia

Work Packages

A structured research program in five parts.

The GeoAI project is divided into five interconnected work packages that systematically develop advanced geospatial artificial intelligence methods.

Content

The project addresses the challenges posed by new geospatial technologies for mass spatial data acquisition and geospatial artificial intelligence, with a specific focus on spatial modeling to support the management of the built and natural environment.

Objectives

The objective is to develop innovative methods for Earth observation, modeling of spatial phenomena, and geospatial analytics by integrating modern geospatial technologies, artificial intelligence, and big geospatial data processing capabilities.

Specific Objectives

(i)
to develop process models for the use of state-of-the-art technologies for mass geospatial data acquisition and Earth observation, focusing on the use of open data and cost-effective technologies for data acquisition with varying temporal and spatial resolutions;

(ii)
to develop and validate new machine learning methods for (semi-)automated mapping and spatial modeling, with a particular focus on scientific challenges related to geospatial data fusion and advanced machine learning;

(iii)
to design and develop innovative approaches for 3D spatio-temporal modeling, analytics, and visualization of the built and natural environment at various levels of detail – from local to regional and global levels – focusing on solutions relevant to supporting spatial decisions;

(iv)
to strengthen the research and innovation excellence and technological capabilities of partner institutions in the field of geospatial science and beyond, primarily through an interdisciplinary approach and collaboration;

(v)
to contribute to strengthening the potential for research and innovation in the geospatial field through use cases and the dissemination of project results, thereby significantly impacting social development.