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Harnessing the power of crowdsourcing to improve land cover and land-use information (CrowdLand)
Date du début: 1 avr. 2014, Date de fin: 31 mars 2019 PROJET  TERMINÉ 

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. Furthermore, 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 amount of observed land cover change also serves as an important indicator of how well different regional, national and European policies have been implemented.However, outside Europe and outside the developed world in particular, information on land cover and land cover change in poorer countries is hardly available and no national or regional dense sample based monitoring approaches such as LUCAS exists which deliver sufficiently accurate land cover and land cover change information. Moreover in particular in developing countries, there is no or very little information on land-use and crop management. Only very limited data available from FAO and an incomplete coverage of sub-national statistics (e.g. IFPRI) are available.This research project will assess the potential of using crowdsourcing to close these big data gaps in developing and developed countries with a number of case studies and different data collection methods. The CrowdLand project will be carried out in two very different environments, i.e. Austria and Kenya.The overall research objectives of this project are to 1) test the potential of using social gaming to collect land use information 2) test the potential of using mobile money to collect data in developing countries 3) understand the data quality collected via crowdsourcing 4) apply advanced methods to filter crowdsourced data in order to attain improved accuracy.