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The paper at hand introduces a novel system for producing an enhanced semantic map that leverages a reconstruction approach of street-view scenes using computer vision and machine learning techniques. Focusing on the recognition and localization of objects/entities, the composed map combines semantic information from publicly available, yet of lower accuracy, satellite images, with more detailed data from ground-level camera measurements. This merging is achieved by utilizing odometry information from a street-moving vehicle and the 3D reconstruction of its recorded view. Then, the 3D semantic segmentation results are georeferenced and superimposed on the semantic map from the satellite images. In such a way, areas that require fine semantic accuracy can be improved, while the rest are left with the segmentation results of the satellite information. Every part of the proposed system is individually …