# Automatic Understanding and Mapping of Regions in Cities Using Google Street View Images
## 2 Related Work  
Maps are important for many applications, such as urban studies, tourism, and autonomous driving. Different techniques have been developed in recent years for the generation of maps applied to different fields, as described in [10], where the authors report that semantic mapping is a growing and active research topic. Below, we review various research works that perform map generation for different purposes. The first work is presented by [PERSON] et al. in [31]. In this work, a method is proposed to derive detailed land-use information at the building block level, based on scene classification algorithms and GSV images. This study is interesting because land-use maps are important references for urban planning and studies. To implement this approach, the authors use GSV images and machine learning techniques, such as image feature descriptors and support vector machines (SVM). The use of GSV images has also been tested for determining demographic levels in residential areas of the USA, as presented in [2], where the authors detect and analyze cars in the images. According to the authors, this proposed method will provide a new method of building-block-level land-use mapping. Additionally, [PERSON] et al. [3] used it as an image source for creating a dataset [4] for vulnerability analysis of buildings [3]. These works focus on images of a building in order to assign typological parameters to the building, and thus determine its vulnerability index. The use of images in map generation procedures involves several sources of images; works such as [6; 7] focus on taking aerial images to achieve semantic segmentation by applying convolutional neural networks (CNNs). These works produced maps considering only the top view of buildings. CNNs are also used for building hierarchical semantic maps to give a robot the capacity to interact with humans, using voice commands for indoor environments [18]. As is known, different methods and tools exist for map generation. Such is the case of [PERSON] et al. [5], who construct high-quality city-scale maps from large collections of GPS trajectories, using graph-based clustering techniques with prior knowledge on road design. This allows for up-to-date and accurate digital maps. In the case of [32], authors use LiDAR sensors to detect the roads in the city streets with a semantic segmentation approach. As an external tool, the use of Global Information Service (GIS) for developing topological maps and navigation for urban robots is evaluated by [PERSON] et al. in [33] for use with mobile robots. Furthermore, the use of topological mapping is presented in studies such as [8; 9; 34; 35; 36]. These works involve the generation of the graph of the location and the construction of the map using different techniques, such as growing neural gas (GNG), SLAM, and analyzing the information provided by the sensors. These maps allow robots to understand, predict, and navigate inside the buildings. Robot navigation has been improved by the use of several mechanisms for generating a semantic map of the environment. The authors in [11; 12; 36; 37] generate semantic maps using 3D information with SLAM methods, sensors, and natural language descriptions, respectively. These maps allow the robots to move over determined areas. Semantic mapping has been studied in surveys [38; 39], where the authors present a diversity of methods for generating semantic maps, mostly, however, applied to indoor environments. Consequently, several algorithms focus on solving semantic mapping for indoor locations, using robots. For outdoor scenarios, in contrast, the problem is not yet solved and the focus of several projects is to determine whether indoor methods might be applied in the same way for outdoor locations. Over the last few years, the generation of semantic maps has focused mainly on the use of neural networks and other techniques with the aim of creating a label-based description for a set of images [17; 18; 19; 21; 40].