Tobias Sturn
Research Scholar
Novel Data Ecosystems for Sustainability Research Group
Advancing Systems Analysis Program
Biography
Tobias Sturn received his Master's degree from the FH Technikum Vienna in Game Engineering and Simulation in 2010. He then worked as project assistant at the Computer Graphics Institute at TU Vienna. In the Landspotting Project he has been collaborating with IIASA to design and develop serious games for improving global land cover. He joined the Ecosystems Services and Management Program (ESM) at IIASA in April 2014, to design and develop serious games like Cropland Capture, Picture Pile or FotoQuest Go.Mr. Sturn's interests cover human-computer interaction, computer graphics and all aspects of gaming, especially games with a purpose and edutainment. With his game development company Emoak he has published a number of games like 'Let's Dance' or 'Paper Climb'. The latter game has been finalist for the Content Award and Future Zone Award. With the help of his latest serious game development 'Cropland Capture' more than 4 million square kilometers of global land surface have already been validated to date by players to improve global land cover maps. The game has been widely featured in the media such as by NPR and The Guardian.
Last update: 13 NOV 2020
Publications
Crocetti, L., Soja, B., Kłopotek, G., Awadaljeed, M., Rothacher, M., See, L. , Weinacker, R., Sturn, T., McCallum, I. , & Navarro, V. (2022). Machine learning algorithms for global modelling of Zenith Wet Delay based on GNSS measurements and meteorological data. In: 1st Workshop on Data Science for GNSS Remote Sensing, 13-15 June 2022, Potsdam, Germany.
Salk, C., Moltchanova, E., See, L. , Sturn, T., McCallum, I. , & Fritz, S. (2022). How many people need to classify the same image? A method for optimizing volunteer contributions in binary geographical classifications. PLOS ONE 17 (5) e0267114. 10.1371/journal.pone.0267114.
See, L. , Laso Bayas, J.C. , Lesiv, M. , Shchepashchenko, D. , Danylo, O., McCallum, I. , Dürauer, M., Georgieva, I., Domian, D., Fraisl, D. , Hager, G. , Karanam, S., Moorthy, I., Sturn, T., Subash, A., & Fritz, S. (2022). Lessons learned in developing reference data sets with the contribution of citizens: the Geo-Wiki experience. Environmental Research Letters 17 (6) e065003. 10.1088/1748-9326/ac6ad7.
Crocetti, L., Soja, B., Klopotek, G., Awadaljeed, M., Rothacher, M., See, L. , Weinacker, R., Sturn, T., McCallum, I. , & Navarro, V. (2022). Machine learning and meteorological data for spatio-temporal prediction of tropospheric parameters. In: EGU General Assembly 2022, 23-27 May 2022, Vienna.
Crocetti, L., Soja, B., Klopotek, G., Awadaljeed, M., Rothacher, M., See, L. , Weinacker, R., & Sturn, T. (2022). Machine learning based modelling of tropospheric parameters with GNSS enhanced by meteorological data. In: ESA Living Planet Symposium, 23-27 May 2022, Bonn, Germany.