GeoCrow - GeoSemantic and Crowdsourced enhanced Virtual Reality for Situational Awareness

The project „GeoCrow“ addresses the current difficulties to gather highly accurate and up-to-date information for military and humanitarian purposes on selected areas of operations. In addition, visualization of the information requires an overlay of topographical and attributive information, in order to provide soldiers information on the topography and the security level in the region. Contemporary Virtual Globes, like Google Earth, Google Earth, Bing Maps, NASA World Wind, provide ample functionalities to virtually discover the topography of the Earth or selected geographical regions. Nevertheless, currently it is hardly possible to integrate mission relevant information from the World Wide Web or even social medial in Virtual Globes or other online maps. This digital reconnaissance is highly relevant for the Austrian Ministry for Defense, as a high number of military missions take place in foreign regions. For such mission it is still difficult to collect local, up-to-date information, such as local natural hazards (avalanches, floodings), social unrest, and aggression against the local population. By crawling the World Wide Web it is possible to collect relevant information/data on the operation area. A semantic enrichment of those data, amended by categorizing, and georeferencing, opens up the possibilities to make use of structured and georeferenced information for the operation. The results of GeoCrow are integrated in a demonstrator implementation that is capable of visualizing operation relevant data with the help of 3D maps embedded in a Virtual Reality environment. The development of Virtual Reality algorithms concerning movement and visualization are research foci here in order to generate a realistic representation of the operations area. Hence, soldiers can virtually explore operations area with the help of the Virtual Reality equipment – and have topographical as well as accurate, up-to-date information at hand. The methodological results of GeoCrow are tested and evaluated based on two scenarios. Scenario#1 deals with humanitarian relief in the area of Iran, whereas scenario#2 deals with an initial reconnaissance mission for a humanitarian operation in the areas of N’Djamena and Abéché (Chad, Africa).

Duration: 01 February 2023 - 31 January 2025


Semantic Web Company TU Wien - Wikipedia Bundesministerium für Landesverteidigung - Wikiwand   Universität für Weiterbildung Krems – Wikipedia   


This project is funded by Österreichische Forschungsförderungsgesellschaft mbH (FFG) within the programme “FORTE, FORTE, FORTE - Kooperative F&EProjekte 2021/2022” Project# FO999895161

ABM4EnergyTransition: Agent-Based Simulation Of Transition Scenarios For Regional Heating And Energy Transformation

Our energy system is undergoing rapid change. New technologies and opportunities such as electromobility, digitization, energy communities or low-temperature heating networks are entering the market, which must be reconciled with societal demands for greater sustainability and political commitments such as the Austrian heating strategy with the goal of decarbonizing the heat supply for buildings by 2040. This poses a high level of complexity for decision-makers in politics, energy supply and business. An energy-efficient and climate-friendly heat supply of the future now requires planned action, the modernization of the existing building stock, a sustainable orientation of new buildings, and the conversion of the heating and cooling supply to renewable energy sources. Not only will infrastructure and technology change, but the use of buildings will also be transformed by digitalization, and administrative as well as planning processes must do justice to this system complexity. In addition to the spatial dimension of energy supply and demand, increasing importance must also be attached to the social dimension of the energy transition in planning and decisionmaking processes. The behaviors and decision-making patterns of central actors (politics, public administration, planners, investors, energy supply companies, population), each with their own specific roles, needs and values, are highly relevant to planning in the context of heat and energy transition, but are, if at all, hardly considered in current processes. This combination and integration will be addressed in the planned project complementing existing (spatial) planning approaches and tools by essential elements.

(c) GISolutions e.U.

(c) GISolutions e.U.

The concrete project objective of ABM4EnergyTransition is the development and demonstration of a novel simulation approach based on agent-based modeling (ABM) for the spatial analysis and assessment of pathways for the municipal heat and energy transition. The ABM methodology considers both spatial data to describe the energy system of a study area (buildings, grid-based energy infrastructure such as natural gas or district heating networks, renewable potentials, population structure) and parameters to describe the behavior of actors (agents) within this energy system (homeowners, investors, policy makers) taking into account demographic and socio - technical parameters such as income and state of education. The methodological approach represents an extension of existing energy planning approaches and helps to better assess the impact of public policies on the achievement of climate and energy goals. The tangible project result will be a prototypical web application with map representation of buildings and energy infrastructure incl. dashboard using two study areas, which allows users from administration and planning to plan possible energy policy and/or technical interventions in the ABM and to display the results (e.g.: Energy and Life Cycle Assessment as well as technology and energy carrier mix in variable spatial and temporal granularity, etc.). The results can be exported for further planning use, e.g. in energy space planning or district heating expansion planning, and in a local GIS system. In addition, simplified simulation scenarios can also be initialized and visualized by interested citizens in the web application for a playful examination of the topic of heat transition and energy system change.

Duration: 01 November 2022 - 31 October 2024



This is funded by Österreichische Forschungsförderungsgesellschaft mbH, FFG (Österreich) within the programme “AI for Green 2021” (Project Number FO999892237)

IGNITE: Improved Assessment of Forest Fire Susceptibility

The moisture content of combustible soil material in forests is the critical element for estimating the risk of forest fires starting. The Canadian Fire Weather Index (FWI) currently used in Austria provides unsatisfactory results with regard to the prediction quality of forest fires. Within the scope of this project, the FWI is to be improved for the whole of Austria and the estimation of the risk of forest fires is to be optimized. The basis for this are i) in-situ measurements in different forest types, ii) ignition experiments in the center on the mountain, iii) the creation of a spatially high-resolution (100x100m²) vegetation index for the risk of forest fires developing, taking into account tree species, gaps, litter moisture and topography, and iv) the validation/reparameterization of the FWI using existing forest fire data, the empirical data obtained and causal machine learning approaches. Final products are a map of forest fire occurrence risk based on vegetation, an optimized FWI, and a new cumulative approach to forest fire risk assessment that will be made available to the general public via the ZAMG homepage.

Duration: 01 April 2022 - 30 September 2024



Forstförderungen Bundeswaldfonds

This project is funded by the Austrian Waldfonds within “Maßnahme 6: Waldbrandprävention”.

PV4EAG: Analysis Of Area And Energy Potential Using AI for Alternative PV Systems as a Contribution to the EAG

With the Renewable Expansion Act (EAG), the goal set by the federal government to cover 100% of total electricity consumption from 2030 on a national balance sheet from renewable energy sources is to be implemented. For this purpose, the annual electricity generation from renewable sources is to be increased by 27 TWh by 2030 through new construction, expansion and revitalization, with 11 TWh being realized by means of photovoltaic systems (PV). In 2019, the installed PV capacity in Austria was 1.7 GWp (1.7 TWh of generated energy). The annual growth rates in the years 2014-2018 were 150-180 MWp/a. Only in 2019 was it possible to achieve a higher value of 250 MWp/a. To achieve the target of 11 TWh by 2030, an annual expansion rate of 1,000 MWp/a would have to be achieved. Current studies show that the available roof areas with an expansion potential of 3.5 TWh are not sufficient and therefore alternative PV systems are required. For the reasons mentioned above, in addition to the planned roof systems, innovative alternative area potentials for PV systems must be tapped. The reports from other projects show the available space and energy potential, but these were determined using statistical methods, so that no statement is made regarding the actual suitability. Alternative PV areas/yields in a city were specified, but no methodically systematic localization of these places was carried out. The motivation of PV4EAG is to close this gap and to develop a process for the automated discovery of alternative PV areas. This addresses the sub-topic “Creation of a freely accessible data platform with energy-relevant forecasts”.

In the PV4EAG project, alternative PV areas are to be analyzed using methods from the field of Geographic Information Science & Technology and artificial intelligence for their suitability in terms of shading, yield potential, construction costs, and grid connection options. The starting point is the existing GIS data (digital building heights, vegetation models, terrain models from airborne laser scanning data, cadastral data), in combination with other publicly available data (Open Governmental Data) and other open data sources (OpenStreetMap, Corine Landcover, etc.), as well as remote sensing data and products (ie orthophotos and satellite image data) are used. By using smart data fusion, semantic annotation of the data (for semantic data integration), and spatial analysis techniques paired with artificial intelligence (GeoAI in the broadest sense), the data should be suitable for large-scale facade-integrated systems on high-rise buildings, sealed parking spaces in shopping centers and residential areas , Traffic areas and rail systems as well as floating PV are analyzed. For the development and testing of the analysis method, the project is limited to selected locations in Styria, with the aim of scaling it up. For these locations, a plausibility check is carried out using the existing VR installation in the EAS lab using a virtual 3D process, and detailed project planning is used to specify the realistic PV yield to be expected.

Duration: 01 January 2022 - 31 December 2023





This project is funded by Österreichische Forschungsförderungsgesellschaft mbH (FFG) within the programme “Energieforschung (e!MISSION), Energieforschung, Energieforschung 7. Ausschreibung” (Project Number FO999888491)

dTS: Data-Driven Tourism for Sustainability

While new digital solutions might provide new challenges, the same technologies might provide solutions to the challenge of creating sustainable and resilient tourism for the future, which strengthens and supports regional, national, and European strategies. With the practical application of these technologies, Austria and its regions have the potential to position themselves on a national and international level as an innovative and sustainable destination. In addition, via the use of data-driven innovation, the tourism sector can increase resilience concerning the before-mentioned disruptive forces, as these innovations and underlying technologies increase flexibility and adaptive capacity to mitigate and conquer impacts on business models and the overall ecosystem. Equally important is the accompanying and steering development of destination management concepts, as well as concepts for the scaling of project results for a strategic, sustainable development of regions. A particular focus here is on avoiding the disadvantaging of vulnerable or disadvantaged groups using technology. The dTS project approaches the challenge of intelligent data use for future-oriented development of tourism regions from a digital sustainability perspective, leveraging digitalization and data-driven technologies to address a) ecological, b) economical, c) and societal challenges from a transdisciplinary perspective in the domain of regional tourism. The main goal for the dTS project is to use the combination of AI and agent-based modelling/simulation to contribute to resilient and sustainable regional tourism in Austria on the example of visitor flow control. The active control and management of visitor flows based on the intelligent use of data can help to sustainably alter the behavior of tourists and lead to a better balance of capacities in the long run. Two use cases in the Land Salzburg have been selected with the aim of strengthening the entire region, exhaust mobility resources, and work towards carbon-neutrality. Therefore, the technical developments within the project need to be accompanied by a sustainable point of view to ensure its effectiveness and optimal integration within the regional tourist ecosystem. At the same time, all technical solutions will be following privacy-by-design patterns and strategies concerning the overall architecture, algorithms, data handling, as well as interaction with the system as a whole. dTS proposes a scalable and portable model for resilient and sustainable tourism. To achieve the development of such a framework, two use cases with different technological maturity levels are being examined. The result will be the design of a scalable data exchange and simulation platform that is also capable to serve as a data circle for visitor flows. Using artificial intelligence and agent-based simulations, the authentic movement and behavior patterns of the target groups are to be learned and understood to then incorporate these findings in the sense of a sustainable and gentle mobility concept. This also enables the modelling of “what-if” scenarios in the sense of decision support for the respective administrations. The main elements of this investigation will be the development of models for fair AI-based predictions and agent-based simulations as well as the application of a federated data management platform approach.

Duration: 01 December 2021 - 31 May 2024



Funded in the programme “ICT of the Future” by the Federal Ministry for Climate Action, Environment, Energy, Mobility, Innovation and Technology (BMK) under grand no. FO999887513


There are many historical places for which geographical extents are unknown, uncertain, or contested in some combination. Historical scholars from many disciplines in the humanities and social sciences are increasingly trying to map and analyze the geographies of past places, at scales from the urban to the regional, from limited available information.

This project will focus on historical regions which are geographically vague in a way that their extents are unknown. The cause for this is that these regions are often only mentioned in historical texts that describe their constituent places and their relationships to other places. For example, a historical text may describe settlements in sufficient detail that can be used to deduce their locations and the administrative regions they belong to, but the extents of the administrative regions themselves are not described. This is problematic because it hinders many potential use cases and future research uses of historical data.

In terms of spatial computation, this is a problem of estimating the geographical extents of a region from locations of geographic entities that are attested to have been within that region. Historical regions will often have shared boundaries with their neighboring regions. In such cases, the inherent uncertainty of an estimated boundary from one region will combine with those of its neighbors. This is where the theory of fuzzy sets can be applied to show that we cannot be certain where one region ends and another starts.

Another issue is the representation of the uncertainty of geographical information on maps and other cartographic visualization products. GIScience experts are trained to understand that all geographical information comes with a level of spatial uncertainty and may consider this when analyzing maps. However, in digital humanities, the representation of uncertainty becomes more important to prevent the users from misinterpreting the data. Historical data also have a temporal component which increases the complexity of boundary estimation and visualization.

Historical regions’ constituents may come from different sources and periods, showing how the region’s extents changed over time. This temporal variation also needs to be addressed in the methodology and visualization approach of this project.


  1. Karl Grossner, PhD
  2. Dr. Eric Delmelle

Funded by:

Cartography and Geographic Information Society