# Automatic Understanding and Mapping of Regions in Cities Using Google Street View Images
## 1 Introduction  
People have always needed to know about the place they live in and the area within which they move, and have hence represented the known space in writing or through drawings. Maps are scale representations of a territory, which, when developed with metric properties, help measure different parameters with great accuracy. A semantic map is a kind of map that adds information about the main role of a place. A map can provide useful data for different activities, such as the analysis of urban plans. Thanks to technological and scientific advances, everyone with Internet access can find free and interactive maps of all kinds, such as Google Maps and applications built over them, such as Google Street View (GSV). The latter is used in research such as [1], where it contributes to the design and evaluation of a new tool for the semiautomatic detection of ramps in GSV images using computer vision and machine learning. GSV has been used as a tool for others works, such as [2; 3; 4], as its utility for scene-understanding approaches has thus been demonstrated. Many researchers have focused their attention on automatic map generation and outdoor scene understanding, using different techniques to perform these tasks. In this sense, Google Street View is currently one of the most important systems, providing us with easy visualization of an area. Different map generation procedures currently focus on obtaining maps with several information levels, such as the semantic level [5; 6; 7; 8; 9]. These mapping procedures have been widely applied for indoor approaches using maps for robot navigation tasks [10; 11; 12]. Outdoor semantic map generation [13; 14; 15; 16], on the other hand, produces fewer, but not worse results. Off-road paths in forests or non-urban areas are the primary focus of outdoor maps [13]. Nevertheless, urban regions will continue to be a challenge for these mapping procedures. A common trend for generating semantic maps consists of using GSV [1; 2; 3] as an image source for the maps. Additionally, indoor or outdoor maps are built by applying supervised and unsupervised learning techniques, mainly convolutional neural networks [13; 16; 17; 18; 19; 20; 21] and clustering algorithms [5; 22], respectively. However, outstanding results can be generated when using both approaches in conjunction [20; 21; 23; 24]. It is worth noting that the semantic approach can today be viewed at two levels: at pixel level [20; 25] and image level [23; 26]. The first focuses on semantic maps for autonomous navigation, whereas the second is applied to navigation and localization within cities. The method proposed in this paper is based on the latter approach. In recent years, semantic localization has been achieved using deep learning models and semantic descriptors [20; 27; 28; 29]. These descriptors are generated using pretrained convolutional neural networks (CNN) models, such as those trained with the ImageNet dataset [30]. Each descriptor consists of a set of labels and their respective probability value, calculated by the CNN model. This kind of descriptor provides an additional description capability that allows for easy human understanding based on the meaning of the labels produced by the model. Then, applying an unsupervised learning algorithm, semantic groups are generated, based on the similarities provided by the labels. The above methodology could be applied to outdoor scenarios. In this case, the places to be labeled involve residential areas, parks, or highways. In order to apply this concept to cities, a major drawback is the difficulty of finding a city dataset that contains images for the majority of the streets in the city. To overcome this problem, the present work will use images captured by GSV, and apply supervised (CNNs) and unsupervised (clustering) machine learning algorithms to create a semantic map that indicates the role of the different zones inside a town. This zone-labeling procedure will be addressed in an autonomous fashion, relying on the information provided.