Geo-Wiki.Org: The Use of Crowdsourcing to Improve Global Land Cover
AbstractGlobal land cover is one of the essential terrestrial baseline datasets available for ecosystem modeling, however uncertainty remains an issue. Tools such as Google Earth offer enormous potential for land cover validation. With an ever increasing amount of very fine spatial resolution images (up to 50 cm × 50 cm) available on Google Earth, it is becoming possible for every Internet user (including non remote sensing experts) to distinguish land cover features with a high degree of reliability. Such an approach is inexpensive and allows Internet users from any region of the world to get involved in this global validation exercise. The Geo-Wiki Project is a global network of volunteers who wish to help improve the quality of global land cover maps. Since large differences occur between existing global land cover maps, current ecosystem and land-use science lacks crucial accurate data (e.g., to determine the potential of additional agricultural land available to grow crops in Africa), volunteers are asked to review hotspot maps of global land cover disagreement and determine, based on what they actually see in Google Earth and their local knowledge, if the land cover maps are correct or incorrect. Their input is recorded in a database, along with uploaded photos, to be used in the future for the creation of a new and improved hybrid global land cover map.
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Fritz, S.; McCallum, I.; Schill, C.; Perger, C.; Grillmayer, R.; Achard, F.; Kraxner, F.; Obersteiner, M. Geo-Wiki.Org: The Use of Crowdsourcing to Improve Global Land Cover. Remote Sens. 2009, 1, 345-354.
Fritz S, McCallum I, Schill C, Perger C, Grillmayer R, Achard F, Kraxner F, Obersteiner M. Geo-Wiki.Org: The Use of Crowdsourcing to Improve Global Land Cover. Remote Sensing. 2009; 1(3):345-354.Chicago/Turabian Style
Fritz, Steffen; McCallum, Ian; Schill, Christian; Perger, Christoph; Grillmayer, Roland; Achard, Frédéric; Kraxner, Florian; Obersteiner, Michael. 2009. "Geo-Wiki.Org: The Use of Crowdsourcing to Improve Global Land Cover." Remote Sens. 1, no. 3: 345-354.