About Us

GeoWiki was established in 2010 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.

GeoWiki 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, GeoWiki exploits novel data ecosystems in which several actors interact via infrastructure, analytics, and applications to produce, analyze, exchange, and consume data. GeoWiki facilitates the faster diffusion of knowledge derived from various forms of Earth observation, in part by facilitating open data with findability, accessibility, interoperability, and reusability (FAIR) data principles. GeoWiki achieves important impact in contributing to SDG monitoring and implementation while providing inputs to a future framework of indicators. 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.

GeoWiki 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).

Since its inception, GeoWiki has grown rapidly, with currently over 20,000 registered users having contributed more than 20 million image classifications from around the world. Furthermore, the GeoWiki 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, kindly 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.

We manage a suite of infrastructure to support our various desktop and mobile applications. The basic infrastructure relies on 16 cloud servers and five physical servers, located within the EU. A Kubernetes cluster sits on top of these for development purposes. We use the open-source PostgreSQL and POSTGIS database management software for hosting our spatial databases. Furthermore, for hosting Web Map Services we employ the open-source Geoserver and Mapserver platforms. A suite of Azure services are used to manage the entire system. We also rely on several Google cloud services (ie. Google Earth Engine, Google Street View). In addition, we utilize Unity software for most of our mobile applications.

Find several of our apps and numerous other interesting Citizen Science applications at the Citizen Science Network Austria.