Geo-Wiki is an online application for the visualization and crowdsourcing of land cover and land use data, where the first data collection campaigns ran during 2011 and 2012.

Geo-Wiki was originally developed as an online application for viewing different global land cover products (i.e., GLC-2000, MODIS and GlobCover) on top of very high-resolution satellite imagery provided back then by Google Earth. For the first time, users could compare different land cover products in one place, and then view the reality from satellite imagery. If users zoomed into any location, they could view the land cover at pixel level and record whether the GLC-2000, MODIS and GlobCover land cover products were correct or inaccurate. However, there was little incentive to provide this information, and hence, very little data on the accuracy of the land cover products was collected using this approach.

Data from the first Geo-Wiki campaigns © IIASA

Data from the first Geo-Wiki campaigns

New capabilities were then added to Geo-Wiki to collect data in ‘campaign’ mode, where data collection campaigns were run for finite periods of time and prizes were offered to incentivize participation, which included various electronic devices, Amazon vouchers and/or co-authorship on a scientific paper. The first four campaigns were run during 2011 and 2012 as outlined in the table, including the amount of data collected.

Locations of data collected from the four campaigns. (a) Human impact, (b) disagreement; (c) wilderness; and (d) reference. © IIASA

Locations of data collected from the four campaigns. (a) Human impact, (b) disagreement; (c) wilderness; and (d) reference.

The first campaign, entitled ‘Human impact’, was focussed on validating maps of land availability for biofuels, produced by Cai et al. (2010). Using a top-down approach to combine different coarse resolution layers, the additional land available for bioenergy production was estimated to range from 320 to 1411 million ha. To validate these maps, Geo-Wiki was used to collect data on the dominant land cover type and the degree of human impact using a sample of pixels drawn from the land availability map. The results showed that the original estimates of land availability for biofuel production were far higher than they should be. Moreover, the estimates of Cai et al. (2010) included large areas of forest, which were clearly visible from the satellite imagery, yet they should not have been included in estimates of the land available for biofuel production. The results of this crowdsourcing campaign and the downward adjustment of the estimates of land availability for biofuels was presented in Fritz et al. (2013), where the top 10 contributors to the campaign became co-authors of the paper.

Hybrid global land cover map  © IIASA

Hybrid global land cover map 

The second campaign was on land cover disagreement, where the sample for validation by the crowd was taken from areas of disagreement between the GLC-2000, MODIS and GlobCover. The data collected were then used to produce a hybrid land cover map from the three individual products (GLC-2000, MODIS and GlobCover) using geographically weighted regression; the results were presented in See et al. (2015), with the top Geo-Wiki volunteers once again becoming co-authors.

A third ‘Reference data campaign’ was undertaken using the same sample locations as those used to validate the global FROM-GLC land cover map (Gong et al., 2013), with the aim of creating a crowdsourced reference data set for land cover and land use products more generally. These sample pixels also coincided with the wilderness campaign (see below). In contrast to the other campaigns, nine students were recruited by the International Food Policy Research Institute (IFPRI) to collect the data.

Geo-Wiki Wilderness Map 2012 © IIASA

Geo-Wiki Wilderness Map 2012

The fourth campaign returned to the theme of human impact and the characterization of wilderness. Wilderness mapping has traditionally been undertaken using top-down approaches, combining different map layers (e.g., land cover, roads, etc.) with distance measures to map the degree of wilderness or the human footprint. In this campaign, a bottom-up approach was used to collect data on human impact and land cover using Geo-Wiki, where the data were then interpolated to produce a global map of the degree of wilderness. The results were presented at the WILD10 conference in Salamanca, Spain, in 2013, and as a book chapter (See et al., 2016) in ‘Mapping Wilderness’ by Carver and Fritz (2016), with co-authorship once again offered as an incentive.

The data from these early campaigns are described in detail in a paper published in Nature’s Scientific Data (Fritz et al., 2017); the data set can be accessed through the Pangaea data repository.

Cai, X., Zhang, X., Wang, D. (2010). Land availability for biofuel production. Environmental Science & Technology, 45 (1), 334−339.

Fritz, S., See, L., van der Velde, M., Nalepa, R.A., Perger, C., Schill, C., McCallum, I., Schepaschenko, D., Kraxner, F., Cai, X., Zhang, X., Ortner, S., Hazarika, R., Cipriani, A., Di Bella, C., Rabia, A.H., Garcia, A., Vakolyuk, M., Singha, K., Beget, M.E., Erasmi, S., Albrecht, F., Shaw, B., Obersteiner, M. (2013): Downgrading recent estimates of land available for biofuel production. Environmental Science & Technology, 47(3), 1688-1694,

Gong, P. et al. (2013). Finer resolution observation and monitoring of global land cover: first mapping results with Landsat TM and ETM+ data. International Journal of Remote Sensing, 34, 2607–2654.

See, L., Fritz, S., Perger, C., Schill, C., Albrecht, F., McCallum, I., Schepaschenko, D., Van der Velde, M., Kraxner, F., Baruah, U.D., Anup Saikia, A., Singh, K., de Miguel, S., Hazarika, R., Sarkar, A., Marcarini, A.A., Baruah, M., Sahariah, D., Changkakati, T. and Obersteiner, M. 2016. Mapping human impact using crowdsourcing. In: S. Carver and S. Fritz (eds). Mapping Wilderness: Concepts, Techniques and Applications of GIS, pp.89-102. Dordrecht: Springer.

See, L., Schepaschenko, D., Lesiv, M., McCallum,  I., Fritz, S., Comber, A., Perger, C., Schill, C., Zhao, Y., Maus, V., Athar Siraj, M., Albrecht, F., Cipriani, A., Vakolyuk, M., Garcia, A., Rabia, A.H., Singha, K.,  Marcarini, A.A., Kattenborn, T., Hazarika, R., Schepaschenko, M., van der Velde, M., Kraxner, F., Obersteiner, M. (2015): Building a hybrid land cover map with crowdsourcing and geographically weighted regression. ISPRS Journal of Photogrammetry and Remote Sensing, 103, 48-56,

Data Set available from Pangaea as follows: