Tobias Sturn profile picture

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

View more

Kłopotek, G., Pan, Y., Sturn, T., Weinacker, R., See, L. , Crocetti, L., Awadaljeed, M., Rothacher, M., McCallum, I. , Fritz, S. , Navarro, V., & Soja, B. (2024). A Cloud-native Approach for Processing of Crowdsourced GNSS Observations and Machine Learning at Scale: A Case Study from the CAMALIOT Project. Advances in Space Research 10.1016/j.asr.2024.02.055. (In Press)

Orduña-Cabrera, F. , Sandoval-Gastelum, M., McCallum, I. , See, L. , Fritz, S. , Karanam, S., Sturn, T., Javalera Rincón, V. , & Gonzalez-Navarro, F.F. (2023). Investigating the Use of Street-Level Imagery and Deep Learning to Produce In-Situ Crop Type Information. Geographies 3 (3) 563-573. 10.3390/geographies3030029.

See, L. , Soja, B., Kłopotek, G., Sturn, T., Weinacker, R., Karanam, S., Georgieva, I. , Pan, Y., Crocetti, L., Rothacher, M., Navarro, V., Fritz, S. , & McCallum, I. (2023). Collecting volunteered geographic information from the Global Navigation Satellite System (GNSS): experiences from the CAMALIOT project. International Journal of Digital Earth 16 (1) 2818-2841. 10.1080/17538947.2023.2239761.

Soja, B., Kłopotek, G., Pan, Y., Crocetti, L., Mao, S., Awadaljeed, M., Rothacher, M., See, L. , Sturn, T., Weinacker, R., McCallum, I. , & Navarro, V. (2023). Machine Learning-Based Exploitation of Crowdsourced GNSS Data for Atmospheric Studies. In: IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium. pp. 1170-1173 Pasadena: IEEE. 10.1109/IGARSS52108.2023.10283441.

See, L. , Sturn, T., Karanam, S., Subash, A., Fraisl, D. , McCallum, I. , & Fritz, S. (2023). The Picture Pile Platform for Rapid Image Classification: Demonstrating the Potential for Citizen Science and SDG Monitoring. In: ISRSE39, 24-28 April 2023, Antalya, Turkey.