Crowdsourcing In-Situ Data on Land Cover and Land Use Using Gamification and Mobile Technology

Laso Bayas, J.C. ORCID: https://orcid.org/0000-0003-2844-3842, See, L. ORCID: https://orcid.org/0000-0002-2665-7065, Fritz, S. ORCID: https://orcid.org/0000-0003-0420-8549, Sturn, T., Perger, C., Dürauer, M., Karner, M., Moorthy, I., Schepaschenko, D. ORCID: https://orcid.org/0000-0002-7814-4990, Domian, D., & McCallum, I. ORCID: https://orcid.org/0000-0002-5812-9988 (2016). Crowdsourcing In-Situ Data on Land Cover and Land Use Using Gamification and Mobile Technology. Remote Sensing 8 (11) e905. 10.3390/rs8110905.

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Abstract

Citizens are increasingly becoming involved in data collection, whether for scientific purposes, to carry out micro-tasks, or as part of a gamified, competitive application. In some cases, volunteered data collection overlaps with that of mapping agencies, e.g., the citizen-based mapping of features in OpenStreetMap. LUCAS (Land Use Cover Area frame Sample) is one source of authoritative in-situ data that are collected every three years across EU member countries by trained personnel at a considerable cost to taxpayers. This paper presents a mobile application called FotoQuest Austria, which involves citizens in the crowdsourcing of in-situ land cover and land use data, including at locations of LUCAS sample points in Austria. The results from a campaign run during the summer of 2015 suggest that land cover and land use can be crowdsourced using a simple protocol based on LUCAS. This has implications for remote sensing as this data stream represents a new source of potentially valuable information for the training and validation of land cover maps as well as for area estimation purposes. Although the most detailed and challenging classes were more difficult for untrained citizens to recognize, the agreement between the crowdsourced data and the LUCAS data for basic high level land cover and land use classes in homogeneous areas (ca. 80%) shows clear potential. Recommendations for how to further improve the quality of the crowdsourced data in the context of LUCAS are provided so that this source of data might one day be accurate enough for land cover mapping purposes.

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