# Automatic Understanding and Mapping of Regions in Cities Using Google Street View Images
## 7 Conclusions  
Under our approach, the construction of semantic maps of a city can be achieved using images acquired from external tools such as Google Street View, avoiding the necessity of traveling around the entire city capturing the images. These images are of sufficient quality for use with CNN algorithms.  
The semantic maps generated with the proposal are capable of grouping and identifying similar images of the city under the study. The zones generated by the algorithm and the semantic label assigned correspond to the actual function of the locations. The semantic descriptors then allow us to understand the possible role for the different points of the city.  
The method proposed shows the capacity of separating very similar zones that accomplish different functions. These zones are very close to each other or within one another, and the method and the descriptors thus accurately encode the inner features of the region of the city. The distribution of the images in the clusters allows us to determine the maps that best represent the city. Moreover, the smoothing procedure helps to create meaningful maps with well-defined borders of zones.  
The use of GPS coordinates allows the map to be used to define area policies for the city, based on the current content of the image and in the areas of the city and avoiding long periods of data-capturing in the city. Furthermore, an intelligent vehicle could use this system to acquire the semantic knowledge of the area in which it is moving. In doing so, the driver could know, for example, whether they must drive carefully when moving in a residential area or could obtain directions to a commercial area.  
As future work, we will train a CNN model, using a reduced number of categories that only represent outdoor places in a city. The acquisition of the GPS points could also be addressed by means of specialized tools for navigation.  
Conceptualization, M.C. and J.C.R.; methodology, M.C. and J.C.R.; software, M.C. and J.C.R.; validation, J.C.R. and E.C.; formal analysis, J.C.R. and E.C.; investigation, J.C.R. and M.C. and E.C.; resources, M.C. and J.C.R.; data curation, J.C.R.; writing--original draft preparation, J.C.R. and M.C. and E.C.; writing--review and editing, J.C.R. and M.C. and E.C.; visualization, J.C.R.; supervision, M.C.; project administration, M.C.; funding acquisition, M.C. All authors have read and agreed to the published version of the manuscript.  
This work has been supported by the Spanish Grant PID2019-104818 RB-I00 funded by MCIN/AEI/10.13039/501100011033 and by \"ERDF A way of making Europe\". [PERSON] and [PERSON] were supported by the Sistema Nacional de Investigacion (SNI) of SENACYT, Panama.  
Not applicable.  
The data used to support the findings of this study are available from the corresponding author upon reasonable request. Also, these images can be found on Street View Static API ([[https://developers.google.com/maps/documentation/streetview/intro](https://developers.google.com/maps/documentation/streetview/intro)]([https://developers.google.com/maps/documentation/streetview/intro](https://developers.google.com/maps/documentation/streetview/intro)) (accessed on 3 February 2022))  
The authors acknowledge administrative support provided by Universidad Tecnologica de Panama (UTP) and University of Alicante.  
The authors declare no conflicts of interest.