Geo-Wiki provides anyone with the means to engage in monitoring of the earth's surface by classifying satellite, drone or ground-level imagery. Data can be input via desktop or mobile devices, with campaigns and games used to incentivize input. These innovative techniques have been used to successfully integrate citizen-derived data sources with expert and authoritative data to address pressing policy-related questions (e.g. European environmental policy, SDG indicators and more).

Geo-Wiki was established in 2009 at the International Institute for Applied Systems Analysis (IIASA) in Laxenburg, Austria. An early beta-version was developed in partnership with the University of Freiburg, Germany and the University of Wiener Neustadt, Austria.

Geo-Wiki mobilizes the tools of citizen and data science combined with Earth observations to monitor, analyze, and foster progress towards the UN Sustainable Development Goals (SDGs). To realize this vision, Geo-Wiki exploits novel data ecosystems in which several actors interact via infrastructure, analytics, and applications to produce, analyze, exchange, and consume data.

Furthermore, it actively supports the field of citizen science with regard to building both the trust and engagement of a larger segment of society, with citizens becoming part of the scientific process while increasing the overall acceptance of scientific outcomes.

image © NODES

Since its inception, Geo-Wiki has grown rapidly, with currently over 20,000 registered users having contributed more than 20 million image classifications from around the world. Furthermore, the Geo-Wiki toolbox has expanded to include numerous applications which help to address a variety of global challenges (e.g. land use change, food security, pollution and more). In addition, we have many ongoing research projects that rely on and further develop these applications, supported by e.g. the European Commission, the European Space Agency and the Austrian Research Promotion Agency among others.

We increasingly apply AI techniques to a variety of our research challenges. In the past we have used deep learning to detect Amazon deforestation, building up a large image library via crowdsourcing to train deep learning algorithms. In another application, we were testing machine learning algorithms for global modelling of Zenith Wet Delay based on GNSS measurements and meteorological data. Furthermore, we have created a free and open AI image library classification tool, a crowdsourcing platform for efficiently and intuitively classifying images for machine learning. In addition, we were part of the RapidAI4EO Project which was advancing rapid and continuous land monitoring with state-of-the-art AI solutions.

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Geo-Wiki projects

High ILUC-Lot 1 -The High Indirect Land Use Change (ILUC) Lot 1 project aims to quantify feedstock expansion onto land with high carbon stock, as input for the determination of high ILUC fuels. The project provides technical assistance to and is financed by the European Commission (DG-Energy).

LandSense - The LandSense Citizen Observatory aims to aggregate innovative EO technologies to empower communities to monitor and report on their environment.

GROW - A European-wide project engaging thousands of growers, scientists and others passionate about the land. We will discover together, using simple tools to better manage soil and grow food.

WeObserve - WeObserve is an H2020 Coordination and Support Action (CSA) which tackles three key challenges that Citizens Observatories (COs) face: awareness, acceptability and sustainability.

Adapt UHI - Urban heat islands and climate change are having an impact on smart urban development. ADAPT-UHI will help to identify targeted mitigation and adaptation measures.

Geo-Wiki projects

FloodCitiSense - FloodCitiSense aims at developing an urban pluvial flood early warning service for, but also by citizens and city authorities. This service will reduce the vulnerability of urban areas and citizens.

Copernicus Global Land Cover - The project aims to develop annual global land cover maps with a 100m resolution consistent over time, and has been tracking land cover changes since 2015.

CIMMYT collaboration - A collaborative project between IIASA and the International Maize and Wheat Improvement Center in Mexico to support efforts of sustainable agricultural intensification with mobile crowdsourcing.

Zurich Flood Resilience - The Zurich Flood Resilience Alliance is a global alliance which aims to improve the resilience of communities around the globe to hazards associated with flooding.

Restore+ - Restore+ combines remote sensing with crowdsourced information on biophysical and social complexity of degraded land. The information will assess restoration options with multi-objective models.

Publications

See, L. , Chen, Q., Crooks, A., Laso Bayas, J.C. , Fraisl, D. , Fritz, S. , Georgieva, I. , Hager, G. , Hofer, M. , Lesiv, M. , Malek, Z. , Milenkovic, M., Moorthy, I. , Orduña-Cabrera, F. , Pérez Guzmán, K. , Shchepashchenko, D. , Shchepashchenko, M. , Steinhauser, J. , & McCallum, I. (2025). New Directions in Mapping the Earth’s Surface with Citizen Science and Generative AI. iScience e111919. 10.1016/j.isci.2025.111919.

See, L. , Fritz, S. , Perger, C., Schill, C., McCallum, I. , Schepaschenko, D. , Dürauer, M., Sturn, T., Karne, M., Kraxner, F., & Obersteiner, M. (2015). Harnessing the power of volunteers, the internet and Google Earth to collect and validate global spatial information using Geo-Wiki. Technological Forecasting and Social Change 98 324-335. 10.1016/j.techfore.2015.03.002.

Fritz, S. , McCallum, I. , Schill, C., Perger, C., See, L. , Schepaschenko, D. , van der Velde, M., Kraxner, F., & Obersteiner, M. (2012). Geo-Wiki: An online platform for improving global land cover. Environmental Modelling & Software 31 110-123. 10.1016/j.envsoft.2011.11.015.