## 7 Conclusions  
This paper described a method for the recognition of urban patterns at different spatial scales in MR and VHR multi-spectral satellite images using machine learning algorithms in the context of a state-of-the-art object-based image analysis. Moving towards a more pattern-oriented approach, which effectively makes use of image domains beyond the spectral content on the pixel level, proved to be a valuable solution when extracting information on the built environment. Particular emphasis was given to an extensive performance analysis of the classification algorithms under varying conditions in order to optimize their usage and assess their applicability to urban pattern recognition tasks. The actual characteristics of the method, including transferability and stability to changes in image type and algorithm parameters were assessed and a performance evaluation framework with focus on object-based image analysis has been proposed to complement the information provided by commonly used accuracy assessments of the final map product. SVM and RT appeared to be the best performing classifiers on all image types according to the predefined performance requirements. KNN and NB showed good performance in some configurations, but mainly lacked the ability to generalize and showed unstable behavior under varying training-testing scenarios.  
Combining quantitative feature selection with machine learning classification algorithms allowed for a high degree of process automation and showed great flexibility regarding transferability of the method to diverse image types and image scenes. In this context, also segmentation parameter tuning proved to be an important step in an object-based image analysis. In comparison to widely used expert systems based on fuzzy set theory, local expert knowledge can be integrated easily into the processing flow by training data selection for which a user does not necessarily need to have detailed knowledge of remote sensing image analysis. Moreover, the use of machine learning allows for easy adjustment and transfer of the method not only to different datasets coming from past and present satellite missions, but also to datasets from upcoming future space-borne missions such as the Sentinel-2 super-spectral satellite of the European Space Agency (ESA) [46] or Digital Globe's WorldView-3 VHR satellite. Performance analysis and algorithm optimization to the lately released Landsat-8 super-spectral MR imagery are currently on going.  
The results of this study indicate great potential of the implemented classification and performance evaluation framework to be integrated into an automated processing chain from data acquisition to user-ready built-up area products. A real-world application of the SVM algorithm, optimized and trained within this study for built-up area recognition from MR and VHR images, can be found in [PERSON], _et al._[47]. The study uses multi-source imaging to assess an exposed building stock and its population as input for a probabilistic seismic vulnerability assessment [48]. The proposed method for training and optimizing classifiers has also successfully been applied to the recognition of more refined urban patterns which outline areas of predominant building types from Landsat TM data, as is described in [PERSON] _et al._[49] and [PERSON] _et al._[50]. Future work will focus on assessing the feasibility of developing an automated processing chain to derive urban information products from multi-spectral satellite images at multiple scales with large (regional or global) coverage.