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Smart city data science: Towards data-driven smart cities with open research issues / Iqbal H. Sarker in Internet of Things, vol 19 (August 2022)  

Public [article]  inInternet of Things > vol 19 (August 2022) . - n° 100528 Titre : Smart city data science: Towards data-driven smart cities with open research issues Type de document : Article/Communication Auteurs : Iqbal H. Sarker, Auteur Année de publication : 2022 Article en page(s) : n° 100528 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle [Termes IGN] apprentissage profond [Termes IGN] gestion urbaine [Termes IGN] internet des objets [Termes IGN] planification urbaine [Termes IGN] science des données [Termes IGN] sécurité [Termes IGN] télédétection [Termes IGN] ville intelligente Résumé : (auteur) Cities are undergoing huge shifts in technology and operations in recent days, and ‘data science’ is driving the change in the current age of the Fourth Industrial Revolution (Industry 4.0 or 4IR). Extracting useful knowledge or actionable insights from city data and building a corresponding data-driven model is the key to making a city system automated and intelligent. Data science is typically the scientific study and analysis of actual happenings with historical data using a variety of scientific methodologies, machine learning techniques, processes, and systems. In this paper, we concentrate on and explore “Smart City Data Science”, where city data collected from various sources such as sensors, Internet-connected devices, or other external sources, is being mined for insights and hidden correlations to enhance decision-making processes and deliver better and more intelligent services to citizens. To achieve this goal, artificial intelligence, particularly, machine learning analytical modeling can be employed to provide deeper knowledge about city data, which makes the computing process more actionable and intelligent in various real-world city services. Finally, we identify and highlight ten open research issues for future development and research in the context of data-driven smart cities. Overall, we aim to provide an insight into smart city data science conceptualization on a broad scale, which can be used as a reference guide for the researchers, industry professionals, as well as policy-makers of a country, particularly, from the technological point of view. Numéro de notice : A2022-383 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE/SOCIETE NUMERIQUE Nature : Article DOI : 10.1016/j.iot.2022.100528 Date de publication en ligne : 20/04/2022 En ligne : https://doi.org/10.1016/j.iot.2022.100528 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100660 [article]

Developing a data-fusing method for mapping fine-scale urban three-dimensional building structure / Xinxin Wu in Sustainable Cities and Society, vol 80 (May 2022)  

Public [article]  inSustainable Cities and Society > vol 80 (May 2022) . - n° 103716 Titre : Developing a data-fusing method for mapping fine-scale urban three-dimensional building structure Type de document : Article/Communication Auteurs : Xinxin Wu, Auteur ; Jinpei Ou, Auteur ; Youyue Wen, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 103716 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] apprentissage automatique [Termes IGN] cartographie urbaine [Termes IGN] données localisées 3D [Termes IGN] données multisources [Termes IGN] fusion de données [Termes IGN] hauteur du bâti [Termes IGN] modèle 3D de l'espace urbain [Termes IGN] modèle de régression [Termes IGN] morphologie urbaine [Termes IGN] Shenzhen [Termes IGN] ville durable [Termes IGN] ville intelligente Résumé : (auteur) Understanding urban morphology is essential for various urban management studies and local environmental issues and guiding sustainable city development. Existing studies mainly focus on analyzing urban morphology from the horizontal aspect, while the urban vertical structure has rarely been discussed due to the scarcity of reliable and fine-scale urban three-dimensional (3-D) building data. This study develops an effective data-fusing methodology to estimate the heights of individual buildings at a city scale. We examined a machine-learning regression model by collecting public materials, including multi-source remote sensing-(RS)-based products, building-derived features, and relevant data to verify its performance in building height estimation. By applying the model in Shenzhen City, a dense city in the Guangdong-Hong Kong-Macao Greater Bay Area, results demonstrated that integrating rich multi-source explanatory variables could achieve high-accuracy building height retrieval. Using multiple building morphological metrics derived by building height data as proxy measures, the urban 3-D form patterns were further analyzed to understand current heterogeneous urban morphological structures. The proposed methodology can be conveniently applied to worldwide cities for urban 3-D morphology retrieval. Also, the available building height information is useful for planners to design morphological control for cities and thus contributes to sustainable and smart city development. Numéro de notice : A2022-268 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/URBANISME Nature : Article DOI : 10.1016/j.scs.2022.103716 Date de publication en ligne : 12/02/2022 En ligne : https://doi.org/10.1016/j.scs.2022.103716 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100279 [article]

Exploring the association between street built environment and street vitality using deep learning methods / Yunqin Li in Sustainable Cities and Society, vol 79 (April 2022)  

Public [article]  inSustainable Cities and Society > vol 79 (April 2022) . - n° 103656 Titre : Exploring the association between street built environment and street vitality using deep learning methods Type de document : Article/Communication Auteurs : Yunqin Li, Auteur ; Nobuyoshi Yabuki, Auteur ; Tomohiro Fukuda, Auteur Année de publication : 2022 Article en page(s) : n° 103656 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG [Termes IGN] apprentissage profond [Termes IGN] attractivité (aménagement) [Termes IGN] bati [Termes IGN] image Streetview [Termes IGN] Japon [Termes IGN] morphologie urbaine [Termes IGN] OpenStreetMap [Termes IGN] piéton [Termes IGN] planification urbaine [Termes IGN] processus de hiérarchisation analytique [Termes IGN] régression linéaire [Termes IGN] scène urbaine [Termes IGN] segmentation sémantique [Termes IGN] système d'information géographique [Termes IGN] urbanisme [Termes IGN] ville intelligente Résumé : (auteur) Street vitality has become an essential indicator for evaluating the attractiveness and potential of the sustainable development of urban blocks, and it can be reflected by the type and the frequency of people's pedestrian activities on the street. While it is recognized that street built environment features affect pedestrian behavior and street vitality, quantifying the impact of these characteristics remains inconclusive. This paper proposes an automated deep learning approach to quantitatively explore the association between the street built environment and street vitality. First, we established a deep learning model for street vitality classification for automatic evaluation of street vitality based on the volumes and activities of pedestrians in the street through multiple object tracking and scene classification. Then, we applied semantic segmentation to measure five selected vitality-related street built environment variables. Finally, a linear regression model was applied to evaluate the built environment variables’ significance and effects on street vitality. To verify our method's accuracy and applicability, we selected a commercial complex in Osaka as an illustrative example. The experimental results highlight that street width and transparency have significant positive effects on street vitality. Compared with traditional methods, our approach is feasible, reliable, transferable, and more efficient. Numéro de notice : A2022-266 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/URBANISME Nature : Article DOI : 10.1016/j.scs.2021.103656 Date de publication en ligne : 10/01/2022 En ligne : https://doi.org/10.1016/j.scs.2021.103656 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100271 [article]

Emerging technologies for smart cities’ transportation: Geo-information, data analytics and machine learning approaches / Li-Minn Ang in ISPRS International journal of geo-information, vol 11 n° 2 (February 2022)  

Public [article]  inISPRS International journal of geo-information > vol 11 n° 2 (February 2022) . - n° 85 Titre : Emerging technologies for smart cities’ transportation: Geo-information, data analytics and machine learning approaches Type de document : Article/Communication Auteurs : Li-Minn Ang, Auteur ; Jasmine Kah Phooi Seng, Auteur ; Ericmoore Ngharamike, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 85 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] apprentissage automatique [Termes IGN] apprentissage profond [Termes IGN] données massives [Termes IGN] planification urbaine [Termes IGN] système de transport intelligent [Termes IGN] trafic routier [Termes IGN] transport collectif [Termes IGN] transport urbain [Termes IGN] ville intelligente [Termes IGN] zone urbaine Résumé : (auteur) With the recent increase in urban drift, which has led to an unprecedented surge in urban population, the smart city (SC) transportation industry faces a myriad of challenges, including the development of efficient strategies to utilize available infrastructures and minimize traffic. There is, therefore, the need to devise efficient transportation strategies to tackle the issues affecting the SC transportation industry. This paper reviews the state-of-the-art for SC transportation techniques and approaches. The paper gives a comprehensive review and discussion with a focus on emerging technologies from several information and data-driven perspectives including (1) geoinformation approaches; (2) data analytics approaches; (3) machine learning approaches; (4) integrated deep learning approaches; (5) artificial intelligence (AI) approaches. The paper contains core discussions on the impacts of geo-information on SC transportation, data-driven transportation and big data technology, machine learning approaches for SC transportation, innovative artificial intelligence (AI) approaches for SC transportation, and recent trends revealed by using integrated deep learning towards SC transportation. This survey paper aimed to give useful insights to researchers regarding the roles that data-driven approaches can be utilized for in smart cities (SCs) and transportation. An objective of this paper was to acquaint researchers with the recent trends and emerging technologies for SC transportation applications, and to give useful insights to researchers on how these technologies can be exploited for SC transportation strategies. To the best of our knowledge, this is the first comprehensive review that examines the impacts of the various five driving technological forces—geoinformation, data-driven and big data technology, machine learning, integrated deep learning, and AI—in the context of SC transportation applications. Numéro de notice : A2022-118 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi11020085 Date de publication en ligne : 24/01/2022 En ligne : https://doi.org/10.3390/ijgi11020085 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99649 [article]

Analytics of location-based big data for smart cities: Opportunities, challenges, and future directions / Haosheng Huang in Computers, Environment and Urban Systems, vol 90 (November 2021)  

Public [article]  inComputers, Environment and Urban Systems > vol 90 (November 2021) . - n° 101712 Titre : Analytics of location-based big data for smart cities: Opportunities, challenges, and future directions Type de document : Article/Communication Auteurs : Haosheng Huang, Auteur ; Xiaobai Yao, Auteur ; Jukka Mathias Krisp, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 101712 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] données issues des réseaux sociaux [Termes IGN] données localisées des bénévoles [Termes IGN] données massives [Termes IGN] planification urbaine [Termes IGN] service fondé sur la position [Termes IGN] téléphonie mobile [Termes IGN] ville durable [Termes IGN] ville intelligente Résumé : (auteur) The growing ubiquity of location/activity sensing technologies and location-based services (LBS) has led to a large volume and variety of location-based big data (LocBigData), such as location tracking or sensing data, social media data, and crowdsourced geographic information. The increasing availability of such LocBigData has created unprecedented opportunities for research on urban systems and human environments in general. In this article, we first review the common types of LocBigData: mobile phone network data, GPS data, Location-based social media data, LBS usage/log data, smart card travel data, beacon log data (WiFi or Bluetooth), and camera imagery data. Secondly, we describe the opportunities fueled by LocBigData for the realization of smart cities, mainly via answering questions ranging from “what happened” and “why did it happen” to “what's likely to happen in the future” and “what to do next”. Thirdly, pitfalls of dealing with LocBigData are summarized, such as high volume/velocity/variety; non-random sampling; messy and not clean data; and correlations rather than causal relationships. Finally, we review the state-of-the-art research trends in this field, and conclude the article with a list of open research challenges and a research agenda for LocBigData research to help achieve the vision of smart and sustainable cities. Numéro de notice : A2021-650 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/URBANISME Nature : Article DOI : 10.1016/j.compenvurbsys.2021.101712 Date de publication en ligne : 08/09/2021 En ligne : https://doi.org/10.1016/j.compenvurbsys.2021.101712 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98368 [article]

Towards culture-aware smart and sustainable cities: Integrating historical sources in spatial information infrastructures / Bénédicte Bucher in ISPRS International journal of geo-information, vol 10 n° 9 (September 2021)  

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Learning from the informality. Using GIS tools to analyze the structure of autopoietic urban systems in the “smart perspective” / Valerio Di pinto in ISPRS International journal of geo-information, vol 10 n° 4 (April 2021)  

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Ecosystem services, green infrastructure and spatial planning / Corrado Zoppi (2021)  

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Intelligent sensors for positioning, tracking, monitoring, navigation and smart sensing in smart cities / Li Tiancheng (2021)  

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Machine learning for the distributed and dynamic management of a fleet of taxis and autonomous shuttles / Tatiana Babicheva (2021)  

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Relation-constrained 3D reconstruction of buildings in metropolitan areas from photogrammetric point clouds / Yuan Li in Remote sensing, vol 13 n° 1 (January-1 2021)  

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Smart cities / Anuar Mohamed Kassim (2021)  

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Urban Informatics / Wenzhong Shi (2021)  

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Social media as passive geo-participation in transportation planning – how effective are topic modeling & sentiment analysis in comparison with citizen surveys? / Oliver Lock in Geo-spatial Information Science, vol 23 n° 4 (December 2020)  

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The Urban Climate Services URCLIM project / Valéry Masson in Climate Services, vol 20 (December 2020)  

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