A global reference database of crowdsourced cropland data collected using the Geo-Wiki platform

Scientific Data
Bayas, Juan Carlos Laso; Lesiv, Myroslava; Waldner, Francois; Schucknecht, Anne; Duerauer, Martina; See, Linda; Fritz, Steffen; Fraisl, Dilek; Moorthy, Inian; McCallum, Ian; Perger, Christoph; Danylo, Olha; Defourny, Pierre; Gallego, Javier; Gilliams, Sven; Akhtar, Ibrar ul Hassan; Baishya, Swarup Jyoti; Baruah, Mrinal; Bungnamei, Khangsembou; Campos, Alfredo; Changkakati, Trishna; Cipriani, Anna; Das, Krishna; Das, Keemee; Das, Inamani; Davis, Kyle Frankel; Hazarika, Purabi; Johnson, Brian Alan; Malek, Ziga; Molinari, Monia Elisa; Panging, Kripal; Pawe, Chandra Kant; Perez-Hoyos, Ana; Sahariah, Parag Kumar; Sahariah, Dhrubajyoti; Saikia, Anup; Saikia, Meghna; Schlesinger, Peter; Seidacaru, Elena; Singha, Kuleswar; Wilson, John W.
SourceWeb of Science
Volume / Issue4
Pages N/A
Total Pages10 pages
Article Link
Editor(s) N/A
Conference / Book Title N/A
Flag N/A
Tagsagriculture; environmental impact; geography; sustainability
Other N/A
Conference Title N/A
Conference Date N/A
Publication DateSeptember 26, 2017
Article Date170136
GS Citation N/A
AbstractA global reference data set on cropland was collected through a crowdsourcing campaign using the Geo-Wiki crowdsourcing tool. The campaign lasted three weeks, with over 80 participants from around the world reviewing almost 36,000 sample units, focussing on cropland identification. For quality assessment purposes, two additional data sets are provided. The first is a control set of 1,793 sample locations validated by students trained in satellite image interpretation. This data set was used to assess the quality of the crowd as the campaign progressed. The second data set contains 60 expert validations for additional evaluation of the quality of the contributions. All data sets are split into two parts: the first part shows all areas classified as cropland and the second part shows cropland average per location and user. After further processing, the data presented here might be suitable to validate and compare medium and high resolution cropland maps generated using remote sensing. These could also be used to train classification algorithms for developing new maps of land cover and cropland extent.
Created: 12/14/2017 10:29 AM (ET)
Modified: 12/31/2018 12:07 PM (ET)
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