Expanding an urban structure type mapping approach from a subarea to the entire city of Berlin

Abstract

Each city exhibits recurring patterns consisting of similar building types, vegetation structures, and open spaces, enabling environmental and socio-economic investigations of the urban fabric. In this study, urban structure types (UST) of the city of Berlin are mapped on the basis of a prior land cover classification utilizing a synergistic approach of knowledge based classification and Random Forests. The results are then compared to the outcomes of a previous analysis regarding a subarea of the utilized high spatial resolution airborne data. Results show that UST classification based on a combination of prototype objects and Random Forests is suitable to generate accurate UST maps for these areas with only minor adaptations. Future analyses will focus on transferring the processes to different German cities and data of several sensors.