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 The GEO-Wiki

  Land cover maps provide critical input data for global models of land use. Urgent questions exist, such as how much land is available for the expansion of agriculture to combat food insecurity, how high will be competition for land between food and bioenergy in the future, as well as how much land is available for afforestation projects?  
Moreover the question arises as to what the most cost-effective mitigation option is (e.g. REDD policies versus biofuel targets). Such questions can only be answered if reliable maps of land cover exist. However, global land cover datasets currently differ drastically in terms of the spatial extent of cropland distributions. One of the data layers, which differs the most is cropland area. Ramankutty et al. 20081 estimate  that, at the 90% confidence range, the cropland area is between 1.22 and 1.71 billion hectares which translates to a 40% difference.
   
Thus, even though global land cover is one of the essential terrestrial baseline datasets available for ecosystem modeling, 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.  The crowd sourcing approach has been growing in use during the last two to three years. Applying it on a global scale in innovative and addressing an issue that would be extremely costly if done in a traditional data gathering approach.
Additional information is available in Fritz, S. et al,  Geo-Wiki.Org: The Use of Crowdsourcing to Improve Global Land Cover. Remote Sens. 2009, 1, 345-354. (http://www.mdpi.com/2072-4292/1/3/345)