Automatic Understanding and Mapping of Regions in Cities Using Google Street View Images

Please use this identifier to cite or link to this item: http://hdl.handle.net/10045/122169
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Title: Automatic Understanding and Mapping of Regions in Cities Using Google Street View Images
Authors: Rangel, José Carlos | Cruz, Edmanuel | Cazorla, Miguel
Research Group/s: Robótica y Visión Tridimensional (RoViT)
Center, Department or Service: Universidad de Alicante. Departamento de Ciencia de la Computación e Inteligencia Artificial | Universidad de Alicante. Instituto Universitario de Investigación Informática
Keywords: Semantic maps | Automatic map | Outdoor understanding | Deep learning
Knowledge Area: Ciencia de la Computación e Inteligencia Artificial
Issue Date: 14-Mar-2022
Publisher: MDPI
Citation: Rangel JC, Cruz E, Cazorla M. Automatic Understanding and Mapping of Regions in Cities Using Google Street View Images. Applied Sciences. 2022; 12(6):2971. https://doi.org/10.3390/app12062971
Abstract: The use of semantic representations to achieve place understanding has been widely studied using indoor information. This kind of data can then be used for navigation, localization, and place identification using mobile devices. Nevertheless, applying this approach to outdoor data involves certain non-trivial procedures, such as gathering the information. This problem can be solved by using map APIs which allow images to be taken from the dataset captured to add to the map of a city. In this paper, we seek to leverage such APIs that collect images of city streets to generate a semantic representation of the city, built using a clustering algorithm and semantic descriptors. The main contribution of this work is to provide a new approach to generate a map with semantic information for each area of the city. The proposed method can automatically assign a semantic label for the cluster on the map. This method can be useful in smart cities and autonomous driving approaches due to the categorization of the zones in a city. The results show the robustness of the proposed pipeline and the advantages of using Google Street View images, semantic descriptors, and machine learning algorithms to generate semantic maps of outdoor places. These maps properly encode the zones existing in the selected city and are able to provide new zones between current ones.
Sponsor: This work has been supported by the Spanish Grant PID2019-104818RB-I00 funded by MCIN/AEI/10.13039/501100011033 and by “ERDF A way of making Europe”. José Carlos Rangel and Edmanuel Cruz were supported by the Sistema Nacional de Investigación (SNI) of SENACYT, Panama.
URI: http://hdl.handle.net/10045/122169
ISSN: 2076-3417
DOI: 10.3390/app12062971
Language: eng
Type: info:eu-repo/semantics/article
Rights: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Peer Review: si
Publisher version: https://doi.org/10.3390/app12062971
Appears in Collections:INV - RoViT - Artículos de Revistas

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