Semantic enrichment of building data with volunteered geographic information to improve mappings of dwelling units and population

Small-scale data on dwellings and population density are required for precise geospatial urban modelling. Further, knowledge of building usage is necessary to model socio-economic aspects such as the distribution of dwellings and population. In an effort to limit costs and resourcing efforts, users and institutes in research and spatial planning are developing strategies to extract such information from existing geographic base data. Currently, land-use information from official datasets merely distinguishes residential from non-residential building usage, but cannot identify areas of non-residential usage inside residential buildings. Additional data sources are therefore needed to fill this gap. In this paper we propose an approach to process semantic information from user-generated OpenStreetMap (OSM) data to specify non-residential usage in residential buildings. This estimation is based on OSM attributes, so-called tags, which are used to define the extent of non-residential usage. Our objective is to identify the potentials and reveal the limitations of integrating semantic OSM data for the evaluation of building usage. Official statistical data on dwellings and population is used to validate results. Thereby we prove the benefit of integrating OpenStreetMap semantic data to refine the estimation of non-residential floor area in the study area of the German City of Dresden, Saxony.