This project is funded by the ERC Consolidator Grant. It assesses the potential of using crowdsourcing to close big data gaps of ground sourced data on land cover, land use and change. The project builds on the Geo-Wiki crowdsourcing tool and moves from an online environment to a mobile ground-based collection system.

Overview: Information about land cover, land use and the change over time is used for a wide range of applications such as nature protection and biodiversity, forest and water management, urban and transport planning, natural hazard prevention and mitigation, agricultural policies and monitoring climate change. High quality spatially explicit information on land cover change is an essential input variable to land use change modelling, which is increasingly being used to better understand the potential impact of certain policies. The overall aim of CrowdLand is to test and demonstrate the potential of using crowdsourcing for land-use related data collection. The project will build upon the success of the Geo-Wiki experience but will move from online data collection via Google Earth or Bing maps to direct in-situ data collection on the ground via smartphones (delivering information on land-use with location, orientation and tilt). A key element will be the idea of a Mobile Guided Point and Direction Finder (MGPDF). This functionality will be a generic feature of the mobile applications built as part of this project in order to help ordinary people to easily find points that are located off-road. The project will discover which incentives are the most appropriate for land-use data collection for different user groups and in different environments, e.g. using the M-pesa system in Kenya and what is the minimum payment needed in order to get citizens involved in data collection. Also, CrowdLand will identify how people are reached (e.g. using ordinary text messaging - SMS) in remote places. Data collection will be carried out by citizen observers and local experts using GPS enabled cameras, smartphones and electronic notebooks. Since information on land cover, land use and change outside of Europe is rarely available in Kenya was selected to case study this project.

IIASA Research: IIASA will determine the feasibility of complementing LUCAS sampling with crowdsourced information via different incentives in Europe. The potential of using mobile money via small payments and other incentives to citizens to replicate LUCAS in developing countries will be examined. The use of mobile phone-based data collection kit (mainly picture-based) given to organizations to monitor success of their programs and actions while at the same time collecting data on global land-use will be investigated. Crowdsourced land-use data in different environments and in different thematic areas using authoritative sources will be assessed. IIASA will also develop new approaches of using point data sources for map data fusion and hybrid map generation, building upon expertise already developed.


Jokar Arsanjani, J., See, L.inda , & Tayyebi, A. (2016). Assessing the suitability of GlobeLand30 for mapping land cover in Germany. International Journal of Digital Earth 9, 1-19. 10.1080/17538947.2016.1151956.

Danylo, O., See, L. , Bechtel, B., Schepaschenko, D. , & Fritz, S. (2016). Contributing to WUDAPT: A Local Climate Zone Classification of Two Cities in Ukraine. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (J-STARS) 9 (5), 1841-1853. 10.1109/JSTARS.2016.2539977.

Lesiv, M. , Moltchanova, E., Shchepashchenko, D. , See, L. , Shvidenko, A., Comber, A., & Fritz, S. (2016). Comparison of Data Fusion Methods Using Crowdsourced Data in Creating a Hybrid Forest Cover Map. Remote Sensing 8 (3), e261. 10.3390/rs8030261.

Salk, C.F., Sturn, T., See, L. , & Fritz, S. (2016). Assessing quality of volunteer crowdsourcing contributions: lessons from the Cropland Capture game. International Journal of Digital Earth 9 (4), 310-426. 10.1080/17538947.2015.1039609.

See, L. , Fritz, S., Perger, C., Schill, C., McCallum, I. , Schepaschenko, D. , Dürauer, M., Sturn, T., et al. (2015). Harnessing the power of volunteers, the internet and Google Earth to collect and validate global spatial information using Geo-Wiki. Technological Forecasting and Social Change 98, 324-335. 10.1016/j.techfore.2015.03.002.

Fritz, S., See, L. , McCallum, I. , Bun, A., Moltchanova, E., Dürauer, M., Perger, C., Havlik, P. , et al. (2015). Mapping global cropland field size. Global Change Biology 21 (5), 1980-1992. 10.1111/gcb.12838.

See, L. , Fritz, S., You, L., Ramankutty, N., Herrero, M., Justice, C., Becker-Reshef, I., Thornton, P., et al. (2015). Improved global cropland data as an essential ingredient for food security. Global Food Security 4, 37-45. 10.1016/j.gfs.2014.10.004.

Bechtel, B., Alexander, P., Böhner, J., Ching, J., Conrad, O., Feddema, J., Mills, G., See, L. , et al. (2015). Mapping Local Climate Zones for a Worldwide Database of the Form and Function of Cities. ISPRS International Journal of Geo-Information 4 (1), 199-219. 10.3390/ijgi4010199.

Perger, C., Dürauer, M., Fritz, S., Bechtel, B., Ching, J., Alexander, P., Mills, G., Foley, M., et al. (2015). Developing a community-based worldwide urban morphology and materials database (WUDAPT) using remote sensing and crowdsourcing for improved urban climate modelling. DOI:10.1109/JURSE.2015.7120501. In: Proceedings, JURSE 2015, 30 March 2015-1 April 2015.

See, L. , Sturn, T., Perger, C., Fritz, S., McCallum, I. , & Salk, C. (2014). Cropland Capture: A gaming approach to improve global land cover. In: Proceedings, AGILE 2014, "Connecting a Digital Europe through Location and Place, 3-6 June 2014.

Print this page

Last edited: 10 July 2018


Steffen Fritz

Senior Research Scholar
EOS Group Leader

Ecosystems Service and Management

T +43(0) 2236 807 353


1.04.2014 - 31.03.2020

International Institute for Applied Systems Analysis (IIASA)
Schlossplatz 1, A-2361 Laxenburg, Austria
Phone: (+43 2236) 807 0 Fax:(+43 2236) 71 313