Historical centers define usually the core of most European or North African cities with different morphology due to climatic differences, topographic adaption, demographic and cultural development, and destruction through totalitarian regimes3 or wars. Comparing buildings outside the old medieval core built over the last 200 years during rapid urban growth (i.e., Wilhelminian or Gruenderzeit era) with suburban neighborhoods, urban morphological characteristics like street width, mean building height, or building density differ substantially. These differences are well observable in satellite imagery with subkilometer resolution or in orthophotos. The major challenge of land surface analysis is to translate these visible differences into comprehensible quantities. Although satellite sensors usually measure data in a spectral range beyond the visible wavelengths, and therefore see many more differences than the human eye, they cannot classify surface structures automatically. Different approaches of characterizing these measurements have been developed: One possibility is to work with different indices to identify and quantify vegetation,4,5 soil,6 buildings,7,8 and water bodies.9 Spectral mixture analysis10–12 and support vector regression,13 to estimate the composition of satellite imagery, are other popular methods. In this study, a classical approach using a maximum-likelihood classification based on ground truth data [regions of interest (ROIs)] is used to subdivide the city into different levels of densification with various characteristics concerning thermal behavior, air movement, morphological parameters, vegetation cover, and many more. This information is crucial for urban development and should be available to planners in a useful manner to enable the distribution of knowledge about basic urban climate aspects among the different disciplines.14–16 Furthermore, LULC analysis data are important for urban climate scientists as an input for models and urban climate studies.