The ‘SpatLab’ Spatial Database Laboratory
About the Laboratory
The Spatial Database Laboratory (SpatLab) is a research and education facility dedicated to advancing methods and technologies for modeling, managing, integrating, processing, analyzing, and preserving spatial data.
The laboratory provides access to a broad range of spatial database technologies, from lightweight desktop database systems to scalable database platforms designed for enterprise and networked environments. By bridging traditional file-based GIS workflows with modern database-driven approaches, SpatLab enables the development of robust, interoperable, and sustainable geospatial data infrastructures.
Our research focuses on relational, object-relational, graph, and emerging database models for geospatial information management. We investigate database architectures, spatial query processing, data interoperability, and advanced methods for integrating heterogeneous spatial datasets across multiple application domains.
The laboratory supports research projects, graduate and undergraduate theses, and collaborative initiatives with industry, public administration, and research institutions. Its activities span geoinformatics, surveying, mining geomatics, environmental engineering, and spatial data science, providing a platform for developing innovative solutions for the next generation of geospatial information systems.
Collaboration and Laboratory Facilities
The Spatial Database Laboratory (SpatLab) provides an environment for research, education, and collaborative development in spatial database technologies and geospatial information management.
The laboratory supports undergraduate, graduate, and doctoral research projects, offering access to modern database technologies for spatial data management, integration, GIS analysis, data modeling, and the application of relational, object-relational, and graph database systems.
SpatLab also serves as a platform for student projects, laboratory classes, technology demonstrators, and specialized training, enabling work with real-world, heterogeneous, and large-scale spatial datasets. The laboratory provides the computational and methodological infrastructure required for research involving data integration, spatial analytics, interoperability, reproducible workflows, and scalable geospatial data management.
Beyond academic activities, SpatLab actively collaborates with public authorities, industry, and external research partners. The laboratory contributes to research and development projects through consulting, technology demonstrators, expert studies, and the design of modern spatial database solutions. Particular emphasis is placed on supporting organizations in the transition from fragmented, file-based data management toward integrated, database-driven geospatial information systems.
SpatLab provides an environment for developing undergraduate and graduate projects in spatial databases, WebGIS, geospatial analytics, and large-scale spatial data processing.
Students have access to modern database technologies, real-world datasets, and professional software development tools. Selected projects and open-source applications are made publicly available to promote reproducible research, software reuse, and collaboration within the geospatial community.
SpatLab develops modern data infrastructures that support the collection, integration, management, analysis, and dissemination of spatial data. Our work enables reliable geospatial information systems for scientific research, infrastructure engineering, and education.
The laboratory focuses on scalable database technologies, interoperable data architectures, and reproducible workflows that facilitate the integration of heterogeneous spatial datasets and their long-term use.
SpatLab supports research and education in Geospatial Computing, providing expertise in spatial databases, geospatial data modeling, and modern database technologies. The laboratory contributes to courses that combine theoretical foundations with hands-on experience in desktop, server-based, and distributed geospatial environments.
Students gain practical experience with contemporary geospatial technologies, including relational, object-relational, and graph database systems, spatial SQL, WebGIS, and scalable geospatial data management.
