Nationwide classification of settlements types using expert knowledge, remote sensing, and open data

The settlement landscape forms an urban-rural continuum between high-density urban centers and open rural areas. Translating semantic settlement types such as urban core or suburban area into space, however, is challenging, as there are no generally accepted definitions for this. In this work, we use expert knowledge, remote sensing, open geodata and machine learning to derive settlement types for Germany. We combine a set of reference points for six settlement types collected by experts with a data cube of 699 descriptive features from remote sensing and other data sources to create a supervised random forest classification model. Our results show a continuous map of settlement types in Germany with high accuracy and high spatial detail. The largest uncertainties in the classification model arise in the distinction between suburban, periurban and rural areas, where the manual classification by experts was also not unambiguous. The result shows that 6.2 % of Germany is covered by urban or suburban settlement types, which are home to 66.8 % of the population.