StarBorn: Towards making in-situ land cover data generation fun with a location-based game

Abstract

Data contributed by a large number of non-experts is increasingly used to validate and curate land cover data, with location-based games (LBGs) developed for this purpose generating particular interest. We here present our findings on StarBorn, a novel LBG with a strong focus on game play. Users conquer game-tiles by visiting real-world locations and collecting land cover data. Within three months, StarBorn generated 13,319 land cover classifications by 84 users. Results show that data are concentrated around users’ daily life spaces, agreement among users is highest for urban and industry land cover, and user-generated land cover classifications exhibit high agreement with an authoritative data set. However, we also observe low user retention rates and negative correlations between number of contributions and agreement rates with an authoritative land cover product. We recommend that future work consider not only game play, but also how motivational aspects influence behavior and data quality. We conclude that LBGs are suitable tools for generating cost-efficient in-situ land cover classifications.