Dueruer, M.

Fritz, S.

Karner, M.

Kraxner, F.

McCallum, I.

Obersteiner, M.

Perger, C.

Schepaschenko, D.

Schill, C.

See, L.

Sturm, T.

International Institute for Applied Systems Analysis (IIASA)

Geophysical Research AbstractsVol. 16, EGU2014-2004, 2014EGU General Assembly 2014© Author(s) 2014. CC Attribution 3.0 License.Cropland Capture: A Game to Improve Global Cropland throughCrowdsourcingSteffen Fritz (1), Tobias Sturn (2), Linda See (1), Christoph Perger (1), Christian Schill (3), Ian McCallum (1),Dmitry Schepaschenko (1), Mathias Karner (1), Martina Dueruer (1), Florian Kraxner (1), and MichaelObersteiner (1)(1) IIASA, Ecosystem Services and Management, Laxenburg, Austria (fritz@iiasa.ac.at), (2) Technical University of Vienna,Vienna, Austria (tobias.sturn@vol.at), (3) Albert-Ludwig University, Freiburg, Germany(christian.schill@felis.uni-freiburg.de)Accurate and reliable global cropland extent maps are essential for estimating and forecasting crop yield, inparticular losses due to drought and production anomalies. Major questions surrounding energy futures andenvironmental change (EU and US biofuel target setting, determination of greenhouse gas emissions, REDDinitiatives, and implications of climate change on crop production and productivity patterns) also require reliableinformation on the spatial distribution of cropland as well as crop types. Although global land cover maps identifycropland (which exist as one or more land cover categories), this information is currently not accurate enough formany applications.There are several ways of improving current cropland extent though hybrid approaches and by integratinginformation collected though Geo-Wiki (a global crowdsourcing platform) from very high resolution imagerysuch as that found on Google Earth. Another way of getting improved cropland extent maps would be to classifyall very high resolution images found on Google Earth and to create a wall-to-wall map of cropland. This isa very ambitious task that would require a large number of individuals, like that found in massive multiplayeronline games. For this reason we have developed a game called ‘Cropland Capture’. The game can be played ona desktop, on a tablet (iPad or Android) or mobile phone (iPhone or Android) where the game mechanics arevery simple. The player is provided with a satellite image or in-situ photo and they must determine if the imagecontains cropland or not. The game was launched in the middle of November 2013 and will run for 6 months, afterwhich the weekly winners will be entered into a draw to win large prizes. To date we have collected more than2.5 million areas, where we will continue to expand the sample to more locations around the world. Eventuallythe data will be used to calibrate and validate a new version of our global cropland map, where the latest versionis available from http://beta-hybrid.geo-wiki.org. If we find, however, that a large number of people participate inthe game, we will aim to make wall-to-wall cropland maps for those countries where no national maps exist. Thispaper will present an overview of the game and a summary of the crowdsourced data from the game, includinginformation about quality and user performance.If successful, this gaming approach could be used to gather information about other land cover types in thefuture in order to improve global land cover information more generally.

Cropland Capture: A game to improve global cropland through crowdsourcing

Cropland Capture: A game to improve global cropland through crowdsourcing

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International Institute for Applied Systems Analysis (IIASA)