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Using street view images to identify road noise barriers with ensemble classification model and geospatial analysis / Kai Zhang in Sustainable Cities and Society, vol 78 (March 2022)
[article]
Titre : Using street view images to identify road noise barriers with ensemble classification model and geospatial analysis Type de document : Article/Communication Auteurs : Kai Zhang, Auteur ; Zhen Qian, Auteur ; Yue Yang, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 103598 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] analyse de groupement
[Termes IGN] apprentissage profond
[Termes IGN] cartographie du bruit
[Termes IGN] Chine
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] distribution spatiale
[Termes IGN] image Streetview
[Termes IGN] lutte contre le bruit
[Termes IGN] milieu urbain
[Termes IGN] OpenStreetMap
[Termes IGN] planification urbaine
[Termes IGN] pollution acoustique
[Termes IGN] trafic routier
[Termes IGN] ville durableRésumé : (auteur) Road noise barriers (RNBs) are important urban infrastructures to relieve the harm of traffic noise pollution for citizens. Therefore, obtaining the spatial distribution characteristics of RNBs, such as precise positions and mileage, can be of great help for obtaining more accurate urban noise maps and assessing the quality of the urban living environment for sustainable urban development. However, an effective and efficient method for identifying RNBs and acquiring their attributes in large areas is scarce. This study constructs an ensemble classification model (ECM) to automatically identify RNBs at the city level based on Baidu Street View (BSV). Firstly, the bootstrap sampling method is proposed to build a street view image-based train set, where the effect of imbalanced categories of samples was reduced by adding confusing negative samples. Secondly, two state-of-the-art deep learning models, ResNet and DenseNet, are ensembled to construct an ECM based on the bagging framework. Finally, a post-processing method has been proposed based on geospatial analysis to eliminate street view images (SVIs) that are misclassified as RNBs. This study takes Suzhou, China as the study area to validate the proposed method. The model achieved an accuracy and F1-score of 0.98 and 0.90, respectively. The total mileage of the RNBs in Suzhou was 178,919 m. The results demonstrated the performance of the proposed RNBs identification framework. The significance of obtaining RNBs attributes for accelerating sustainable urban development has been demonstrated through the case of photovoltaic noise barriers (PVNBs). Numéro de notice : A2022-241 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE/INFORMATIQUE Nature : Article DOI : 10.1016/j.scs.2021.103598 Date de publication en ligne : 20/12/2021 En ligne : https://doi.org/10.1016/j.scs.2021.103598 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100167
in Sustainable Cities and Society > vol 78 (March 2022) . - n° 103598[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)
[article]
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 urbaineRésumé : (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
in ISPRS International journal of geo-information > vol 11 n° 2 (February 2022) . - n° 85[article]Raw GIS to 3D road modeling for real-time traffic simulation / Yacine Amara in The Visual Computer, vol 38 n° 1 (January 2022)
[article]
Titre : Raw GIS to 3D road modeling for real-time traffic simulation Type de document : Article/Communication Auteurs : Yacine Amara, Auteur ; Abdenour Amamra, Auteur ; Salim Khemis, Auteur Année de publication : 2022 Article en page(s) : pp 239 - 256 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] comportement
[Termes IGN] graphe topologique
[Termes IGN] intersection spatiale
[Termes IGN] modèle de simulation
[Termes IGN] modélisation 3D
[Termes IGN] navigation virtuelle
[Termes IGN] planification urbaine
[Termes IGN] système d'information géographique
[Termes IGN] système multi-agents
[Termes IGN] temps réel
[Termes IGN] trafic routier
[Termes IGN] trajectoire (véhicule non spatial)Résumé : (auteur) In this work, we propose a new approach to road modeling and 3D traffic simulation. Based on the raw geographic information system (GIS) data laid out as sparse polylines with attributes, we compute a more adequate functional description for real-time simulation of on-road vehicle animation. The proposed approach begins with a filtering/subdivision module where the raw polylines are transformed into a graph of functional road segments as arcs and the nodes as intersections. Then, the vehicle speed profile is computed based on its dynamics, its neighborhood and the curvature profile of the road. Afterward, a multi-agent system is proposed in order to handle a large number of simulated vehicle/driver couples. Finally, we deploy a 3D rendering engine to display the computed 3D simulation on screen. The resulting model satisfies most of the real road features for traffic simulation including road interchanges, roundabouts, intersections, lanes, etc. More importantly, the simulated driving qualitatively mimics the real behavior of the drivers/vehicles on the road as can be seen in the accompanying video (RTSP video). We also validate our findings with a technical assessment based on macroscopic and microscopic traffic simulation metrics in several road traffic scenarios. Numéro de notice : A2022-160 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1007/s00371-020-02013-1 Date de publication en ligne : 01/01/2022 En ligne : https://doi.org/10.1007/s00371-020-02013-1 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99777
in The Visual Computer > vol 38 n° 1 (January 2022) . - pp 239 - 256[article]Exploring data fusion for multi-object detection for intelligent transportation systems using deep learning / Amira Mimouna (2022)
Titre : Exploring data fusion for multi-object detection for intelligent transportation systems using deep learning Type de document : Thèse/HDR Auteurs : Amira Mimouna, Auteur ; Abdelmalik Taleb-Ahmed, Directeur de thèse ; Najoua Essoukri Ben Amara, Directeur de thèse Editeur : Valenciennes : Université polytechnique Hauts-de-France Année de publication : 2022 Note générale : bibliographie
Thèse de doctorat pour obtenir le grade de Docteur de l'Université polytechnique Hauts-de-France et l'INSA Hauts-de-France et l'Université de Sousse, spécialité Electronique, Acoustique et TélécommunicationsLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage profond
[Termes IGN] classification par réseau neuronal récurrent
[Termes IGN] détection d'objet
[Termes IGN] données publiques
[Termes IGN] entropie
[Termes IGN] profil d'obstacle
[Termes IGN] segmentation d'image
[Termes IGN] système de transport intelligent
[Termes IGN] trafic urbain
[Termes IGN] transformation en ondelettes
[Termes IGN] vision par ordinateur
[Termes IGN] zone d'intérêtIndex. décimale : THESE Thèses et HDR Résumé : (auteur) Building reliable environment perception systems is a crucial task for autonomous driving, especially in dense traffic areas. Researching in this field is evolving increasingly. However, we are at the beginning of a research pathway towards a future generation of intelligent transportation systems. In fact, challenging conditions in real-world driving circumstances, infrastructure monitoring, and accurate real-time system response, are the predominant concerns when developing such systems. Recent improvements and breakthroughs in scene understanding for intelligent transportation systems have been mainly based on deep learning and the fusion of different modalities. In this context, firstly, we introduce OLIMP : A heterOgeneous MuLtimodal Dataset for Advanced EnvIronMent Perception . This is the first public, multimodal and synchronized dataset that includes Ultra Wide-Band (UWB) radar data, acoustic data, narrowband radar data and images. OLIMP comprises 407 scenes and 47,354 synchronized frames, including four categories: pedestrians, cyclists, cars and trams. The dataset presents various challenges related to dense urban traffic such as cluttered environments and differentweather conditions. To demonstrate the usefulness of the introduced dataset, we propose, afterwards, a fusion framework that combines the four modalities for multi object detection. The obtained results are promising and spur for future research. In short range settings, UWB radars represent a promising technology for building reliable obstacle detection systems as they are robust to environmental conditions. However, UWB radars suffer from a segmentation challenge: localizing relevant Regions Of Interests (ROIs) within its signals. Therefore, we put froward a segmentation approach to detect ROIs in an environment perception-dedicated UWB radar as a third contribution. Specifically, we implement a differential entropy analysis to detect ROIs. The obtained results show higher performance in terms of obstacle detection compared to state-of-theart techniques, as well as stable robustness even with low amplitude signals. Subsequently, we propose a novel framework that exploits Recurrent Neural Networks (RNNs) with UWB signals for multiple road obstacle detection as a deep learning-based approach. Features are extracted from the time-frequency domain using the discrete wavelet transform and are forwarded to the Long short-term memory (LSTM) network. The obtained results show that the LSTM-based system outperforms the other implemented related techniques in terms of obstacle detection. Note de contenu : 1- Introduction
2- Environment perception system: State of the art
3- OLIMP: A heterogeneous multimodal dataset for advanced environment perception
4- Multiple object detectors using UWB signals
5- Conclusions and perspectivesNuméro de notice : 15289 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Thèse française Note de thèse : Thèse de Doctorat : Electronique, Acoustique et Télécommunications : Université polytechnique Hauts-de-France : 2022 Organisme de stage : Institut d'électronique, de microélectronique et de nanotechnologie DOI : sans En ligne : https://hal.science/tel-03522730 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101520 GIS-based survey over the public transport strategy: An instrument for economic and sustainable urban traffic planning / Gabriela Droj in ISPRS International journal of geo-information, vol 11 n° 1 (January 2022)
[article]
Titre : GIS-based survey over the public transport strategy: An instrument for economic and sustainable urban traffic planning Type de document : Article/Communication Auteurs : Gabriela Droj, Auteur ; Laurentiu Droj, Auteur ; Ana-Cornelia Badea, Auteur Année de publication : 2022 Article en page(s) : n° 16 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG
[Termes IGN] analyse spatiale
[Termes IGN] modèle mathématique
[Termes IGN] planification urbaine
[Termes IGN] pollution atmosphérique
[Termes IGN] Roumanie
[Termes IGN] trafic routier
[Termes IGN] trafic urbain
[Termes IGN] transport publicRésumé : (auteur) Traffic has a direct impact on local and regional economies, on pollution levels and is also a major source of discomfort and frustration for the public who have to deal with congestion, accidents or detours due to road works or accidents. Congestion in urban areas is a common phenomenon nowadays, as the main arteries of cities become congested during peak hours or when there are additional constraints such as traffic accidents and road works that slow down traffic on road sections. When traffic increases, it is observed that some roads are predisposed to congestion, while others are not. It is evident that both congestion and urban traffic itself are influenced by several factors represented by complex geospatial data and the spatial relationships between them. In this paper were integrated mathematical models, real time traffic data with network analysis and simulation procedures in order to analyze the public transportation in Oradea and the impact on urban traffic. A mathematical model was also adapted to simulate the travel choices of the population of the city and of the surrounding villages. Based on the network analysis, traffic analysis and on the traveling simulation, the elements generating traffic congestion in the inner city can be easily determined. The results of the case study are emphasizing that diminishing the traffic and its effects can be obtained by improving either the public transport density or its accessibility. Numéro de notice : A2022-039 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi11010016 Date de publication en ligne : 30/12/2021 En ligne : https://doi.org/10.3390/ijgi11010016 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99382
in ISPRS International journal of geo-information > vol 11 n° 1 (January 2022) . - n° 16[article]Road traffic crashes and emergency response optimization: a geo-spatial analysis using closest facility and location-allocation methods / Sulaiman Yunus in Geomatics, Natural Hazards and Risk, vol 13 (2022)PermalinkRobust approach for urban road surface extraction using mobile laser scanning 3D point clouds / Abdul Nurunnabi (2022)PermalinkTowards synthetic sensing for smart cities : a machine/deep learning-based approach / Faraz Malik Awan (2022)PermalinkUrban infrastructure audit: an effective protocol to digitize signalized intersections by mining street view images / Xiao Li in Cartography and Geographic Information Science, vol 49 n° 1 (January 2022)PermalinkPermalinkModeling transit-assisted hurricane evacuation through socio-spatial networks / Yan Yang in International journal of geographical information science IJGIS, vol 35 n° 12 (December 2021)PermalinkDouble adaptive intensity-threshold method for uneven Lidar data to extract road markings / Chengming Ye in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 9 (September 2021)PermalinkA multiagent systems with Petri Net approach for simulation of urban traffic networks / Mauricio Flores Geronimo in Computers, Environment and Urban Systems, vol 89 (September 2021)PermalinkFlood depth mapping in street photos with image processing and deep neural networks / Bahareh Alizadeh Kharazi in Computers, Environment and Urban Systems, vol 88 (July 2021)PermalinkThe point-descriptor-precedence representation for point configurations and movements / Amna Qayyum in International journal of geographical information science IJGIS, vol 35 n° 7 (July 2021)Permalink