## 1 Introduction  
The study of the development and change of urban areas has a long history and is gaining more importance due to the acceleration of urbanization in the 20 th century [1; 2; 3; 4]. The way that cities are built and designed influences our societal lifestyles and resource consumption [5; 6; 7]. Moreover, the consumption-driven exploitation of arable land is leading to sometimes even higher biodiversity in diverse urban areas than in agriculturally dominated landscapes [8]. The relatively new research fields of urban ecology and urban ecosystems deal with ecological elements in human-dominated habitats, and specifically, with the benefits from ecosystems to humans in cities [9; 10]. In terms of surface texture, urban areas are the most complex and heterogeneous landscapes on earth--a mosaic and patchy composite of artificial and natural surface under dynamic change [11; 12; 13]. For the benefits of humans, e.g., recreation or local climate regulation, very small ecological patches can be important [12]. Therefore, a detailed analysis of heterogeneous urban areas is only possible with information of high spatial resolution [4]. This analysis is necessary to identify unsustainable urbanization processes, e.g., of environmental degradation, and to guide the sustainable development of urban areas. Remote sensing imagery is commonly used to identify and classify urban areas [7; 14; 15]. In order to distinguish them from other surrounding areas, a spatial resolution of 30 m is often enough. A spatial resolution of 10-20 m allows distinction between residential and industrial areas (intra-urban analysis) [11]. Typical rectangular urban objects, such as buildings, can be detected at a spatial resolution of 2-3 m. Elementary linear objects (e.g., roads or smaller rivers) require a spatial resolution of at least 2 m. Trees or other individual objects are detectable at a resolution between 0.7 m and 1 m. Above these resolutions, these objects are no longer identifiable because they belong to a heterogeneous area due to the increased number of pixels. As a result, individual objects are integrated into their environment [11]. Most land use/land cover (LULC) classifications use satellite images from the NASA/USGS Landsat missions with a spatial resolution of 30 m (15 m for the panchromatic band) or from the European Space Agency Sentinel missions with up to 10 m (5 m for Synthetic Aperture Radar data) spatial resolution. These resolutions allow good results when distinguishing between residential and industrial areas (intra-urban analysis), but they are not sufficient for an inner-urban analysis of building structures, trees, and heterogeneous green spaces [16]. Higher spatial resolutions are needed, especially in developing countries, where urban structures and construction patterns are usually of smaller scales and higher in complexity than in developed countries [11]. Moreover, (informal) urbanization and LULC dynamics are extraordinarily high in developing countries and the deterioration of urban ecosystems, e.g., rivers and their riparian areas, is rapidly advancing [17; 18; 19; 20]. A detailed analysis and assessment of these dynamics would be very helpful to better understand the problem and to develop local solutions for a more sustainable urban development. However, so-called very high spatial resolution (VHSR) images (1 to 5 m) [21; 22] are usually not free of cost, representing popular commercial products. An alternative is the use of freely available, VHSR true-color satellite imagery provided by Google Earth Pro. The cost-free use of the Google Earth software has led, among others, to the discovery of archeological sites and the impact craters of meteoroids [23; 24; 25; 26]. However, the use of true color images provided by Google Earth for detailed urban LULC classification has not been documented so far. A Web of Science Core Collection search (17 September 2019) using the search string ALL FIELDS: (\"Google Earth\" AND \"LULC\" AND \"Urban\") resulted in a total of 10 results.