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Head of the Geoinformation Department
die STEG Stadtentwicklung GmbH
Hamidreza Ostadabbas is the Head of the Geoinformation Department at die STEG Stadtentwicklung GmbH in Stuttgart, Germany, where he leads the development of innovative GIS, GeoAI, and digital twin solutions for urban planning and municipal digitalization.
He is also a Lecturer at the Hochschule für Technik Stuttgart (HFT Stuttgart), teaching courses in GIS Studio, Smart Data Components, and Artificial Intelligence within the Geoinformatics, Photogrammetry, and Smart City Solutions programs. His research and teaching focus on open-source geospatial technologies, GeoAI, mobile GIS, digital data acquisition, spatial databases, 3D city models, and urban digital twins.
Hamidreza is an OSGeo Charter Member and serves as the Deputy Young Surveyors' Representative of the Council of European Geodetic Surveyors (CLGE). He is an active speaker and trainer at international conferences and workshops, including FOSS4G, GeoPython, and other geospatial events.
His current research interests include GeoAI, Large Language Models (LLMs) for GIS, Retrieval-Augmented Generation (GeoRAG), municipal heat planning, digital twins, spatial decision support systems, and open-source geospatial software for sustainable urban development.
Date
This presentation demonstrates a 3D web application for real-time visualization of CO₂ emissions and district heating demand using GeoAI and open geospatial data. By integrating ALKIS cadastral data, CityGML building models, and infrastructure layers within QGIS and PostgreSQL, the system creates an interactive urban digital twin powered by CesiumJS. A custom-built GeoAI model estimates CO₂ emissions and heating demand at the building level, based on attributes such as usage type, construction year, geometry, and roof structure. Users can update these parameters directly in QGIS or remotely via QFieldCloud, with all changes reflected live in the 3D environment. Buildings are dynamically styled based on their CO₂ output or connection status to the district heating network. Key features: GeoAI-powered estimation of CO₂ emissions and heating demandReal-time data synchronization via Django REST APIsIntegration of ALKIS and CityGML for detailed urban modelingVisualization and planning of district heating networksCross-platform data editing using QGIS and QFieldCloud This demo offers a scalable and open-source approach to building interactive 3D digital twins that support climate-responsive planning, energy infrastructure design, and municipal decision-making.