## 1 Introduction
Another search using the search string ALL FIELDS: (\"Google Earth\" AND \"land use\" AND \"classification\" AND \"Urban\") resulted in a total of 26 results. From this total of 36 scientifically index publications, only one publication uses Google Earth imagery for LULC classification, but for a national scale (Maldives) and not specifically for urban areas. So far, the use of Google Earth imagery in published scientific research has been limited to checking land use classification results based on other image sources with coarser spatial resolution (historically Landsat imagery, but more recently Sentinel imagery as well). For many urban areas, worldwide images of spatial resolution below 1 m are provided by Google Earth free of cost for non-commercial uses. These images are exportable as GeoTIFF raster images for further processing in a geographic information system (GIS). As true-color images, they contain the RGB full-color values per raster cell. Contrary to other freely available satellite imagery products, the images provided by Google Earth do not contain layers of different wavelengths of the same image, thus no multi-spectral resolution. Therefore, the possibilities of image analysis are limited to raster cell values of RGB color alone. We are aware that this is a significant limitation to image classification, as different land cover may have very similar colors, and now non-visible bands, such as infrared or near-infrared can be used to distinguish, e.g., between green vegetation and green roofs. However, we believe that the advantage of a very high spatial resolution of images provided by Google Earth Pro can compensate for this disadvantage compared to other freely available and commonly used imagery (e.g., from the Sentinel or Landsat missions). To reveal and discuss the possible advantages and disadvantages for LULC classification was exactly the objective of this study. For that reason, the quality and occurrence of misclassification was specifically addressed and a newly developed post-processing procedure was tested to deal with the disadvantage of having no multispectral resolution of the images. Therefore, a methodology to use Google Earth images for high spatial resolution urban LULC classification for data scare regions is presented. Furthermore, a classification correction procedure (post-processing) is introduced in order to reduce classification errors from clouds, shadows, or temporal features. Finally, the classification accuracy before and after applying the newly developed correction procedure is assessed and compared. The classification was carried out for three neighborhoods of the city of Leon in Nicaragua in order to assess an ongoing urbanization and environmental degradation process. Each neighborhood has unique land cover and urbanization characteristics representing: (images at the bottom of Figure 1, from right to left) (1) a historically grown part of the city, (2) a neighborhood in the transition from urban to rural status, and (3) a developing sub-urban settlement. These classification areas differ in degree of urbanization (building density, i.e., percentage of sealed surface by streets and houses), vegetation cover (diversity and arrangement of green spaces), and proximity to urban rivers. A specific subject of assessment was the accuracy of the classification of those different land use mosaics in order to prove the applicability of the presented method to analyze the spatial and temporal dynamics of environmental degradation and urbanization processes in detail. The resulting LULC classification imagery can be a valuable input to determine urbanization processes, for assessments of ecological status and to estimate the ecological potential of urban areas and urban rivers [27]. Additionally, the high level of spatial details acquired may serve as a basis for the development of nature-based solutions and the design of green infrastructure at the neighborhood level.