This diploma thesis deals with the topic of automatic classification of buildings. The main goal of this diploma thesis was to design an algorithm for the identification of building types for the purposes of cartographic generalization. For the purposes of this diploma thesis, a total of six types of development were defined with respect to different generalization of individual types on ZM 50. The first part of the proposed method is represented by an algorithm for segmenting buildings into clusters based on the use of already generalized road network and DBSCAN algorithm. The partial goal of this diploma thesis was to compare classifiers from the field of machine learning and neural networks and at the same time to compare classifiers using descriptive characteristics with classifiers using visual assessment. The resulting classifications were evaluated using data from a manually selected training set and using an algorithm comparing the resulting type of development with the type of cartographic representation used to represent the development on ZM 50. The whole method was implemented in Python using Arcpy, Scikit-learn and Tensorflow libraries. Testing took place on elements from the ZABAGED and Data50 databases. Keywords: Generalization of built-up areas, Classification, Machine learning,...

Simplification of contour lines in flat areas The diploma thesis is focused on the cartographic generalization of contour lines derived from dense point clouds in flat territories, where the original contour lines tend to oscillate. The main aim is to propose, develop and test a new algorithm for the contour simplification preserving the given vertical error and reflecting the cartographic rules. Three methods designed for large scale maps (1 : 10 000 and larger) are presented: the weighted average, modified Douglas-Peucker and potential-based approach. The most promising method is based on the repeated simplification of contour line segments by calculating the generalization potential of its vertices. The algorithm is implemented in Python 2.7 with the use of Arcpy library was tested on DMR5G data, the simplified contour lines were compared with the result created by a professional cartographer. Achieved results are presented on attached topographic maps. Keywords: contours, cartographic generalization, digital cartography, vertical buffer, smoothing, GIS