Towards Seamless Validation of Land Cover Data

Abstract. This article demonstrates the ability of the Bayesian Network analysis for the recognition of uncertainty patterns associated with the fusion of various land cover data sets including GlobeLand30, CORINE (CLC2006, Germany) and land cover data derived from Volunteered Geographic Information (VGI) such as Open Street Map (OSM). The results of recognition are expressed as probability and uncertainty maps which can be regarded as a by-product of the GlobeLand30 data. The uncertainty information may guide the quality improvement of GlobeLand30 by involving the ground truth data, information with superior quality, the know-how of experts and the crowd intelligence. Such an endeavor aims to pave a way towards a seamless validation of global land cover data on the one hand and a targeted knowledge discovery in areas with higher uncertainty values on the other hand.