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Global Open Data Assessment / Mathis Rouillard (2024)

Public Titre : Global Open Data Assessment Type de document : Mémoire Auteurs : Mathis Rouillard, Auteur Editeur : Champs-sur-Marne : Ecole nationale des sciences géographiques ENSG Année de publication : 2024 Importance : 48 p. Format : 21 x 30 cm Note générale : bibliographie Rapport de fin d'étude, cycle des ING3, spécialisé TSI Langues : Anglais (eng) Descripteur : [Termes IGN] graphe [Termes IGN] OpenStreetMap [Termes IGN] Python (langage de programmation) [Termes IGN] qualité [Termes IGN] réseau routier [Termes IGN] web 2.0 Index. décimale : MTSI Mémoires du Master Technologies des Systèmes d'Information Résumé : Au sein de l’équipe d’ingénieur·e·s de LocationMind Inc., une startup japonaise, un vif intérêt a été porté sur Overture Maps Foundation (OMF), un jeu de données ouvert utilisant notamment des données d’OpenStreetMap (OSM), le jeu de données géographique le plus utilisé dans le monde. Étant donné qu’OMF n’a été publié que récemment, les différences entre OSM et OMF sont encore assez floues. C’est pourquoi essayer de comparer ces jeux de données constitue un défi intéressant, surtout en développant un système de visualisation permettant d’analyser ces résultats sur les réseaux routiers uniquement. Pour ce faire, après avoir choisi des critères de qualité pour comparer ces données, des scripts Python utilisant DuckDB, OSMnx et GeoPandas ont été produits afin d’évaluer la qualité de ces jeux de données, en créant préalablement un modèle de données commun. Un tableau de bord a été choisi pour visualiser les données, s’appuyant sur les technologies Shiny for Python et LonBoard. L’interface réalisée est fonctionnelle et permet d’analyser les résultats sur des zones prédéterminées. Il n’est cependant pas encore possible de comparer pleinement les deux jeux de données, l’évaluation n’ayant été réalisée que sur des zones tests et non sur des pays entiers. Note de contenu : Introduction 1. Contexte and challenges 2. Quality assessment 3. Visualisation system 4. Results and perspectives Conclusion Numéro de notice : 24229 Affiliation des auteurs : IGN (2020- ) Thématique : GEOMATIQUE/INFORMATIQUE Nature : Mémoire de fin d'études IT Organisme de stage : LocationMind Inc. Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=103837

Detecting spatiotemporal propagation patterns of traffic congestion from fine-grained vehicle trajectory data / Haoyi Xiong in International journal of geographical information science IJGIS, vol 37 n° 5 (May 2023)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 37 n° 5 (May 2023) . - pp 1157-1179 Titre : Detecting spatiotemporal propagation patterns of traffic congestion from fine-grained vehicle trajectory data Type de document : Article/Communication Auteurs : Haoyi Xiong, Auteur ; Xun Zhou, Auteur ; David A. Bennett, Auteur Année de publication : 2023 Article en page(s) : pp 1157-1179 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] déformation temporelle dynamique (algorithme) [Termes IGN] détection d'anomalie [Termes IGN] données spatiotemporelles [Termes IGN] événement [Termes IGN] flux [Termes IGN] gestion de trafic [Termes IGN] réseau routier [Termes IGN] trafic routier Résumé : (auteur) Traffic congestion on a road segment typically begins as a small-scale spatiotemporal event that can then propagate throughout a road network and produce large-scale disruptions to a transportation system. In current techniques for the analysis of network flow, data is often aggregated to relatively large (e.g. 5 min) discrete time steps that obscure the small-scale spatiotemporal interactions that drive larger-scale dynamics. We propose a new method that handles fine-grained data to better capture those dynamics. Propagation patterns of traffic congestion are represented as spatiotemporally connected events. Each event is captured as a time series at the temporal resolution of the available trajectory data and at the spatial resolution of the network edge. The spatiotemporal propagation patterns of traffic congestion are captured using Dynamic Time Warping and represented as a set of directed acyclic graphs of spatiotemporal events. Results from this method are compared to an existing method using fine-grained data derived from an agent-based model of traffic simulation. Our method outperforms the existing method. Our method also successfully detects congestion propagation patterns that were reported by media news using sparse real-world data derived from taxis. Numéro de notice : A2023-225 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2023.2178653 Date de publication en ligne : 22/02/2023 En ligne : https://doi.org/10.1080/13658816.2023.2178653 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=103177 [article]

A network-constrained clustering method for bivariate origin-destination movement data / Wenkai Liu in International journal of geographical information science IJGIS, vol 37 n° 4 (April 2023)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 37 n° 4 (April 2023) . - pp 767 - 787 Titre : A network-constrained clustering method for bivariate origin-destination movement data Type de document : Article/Communication Auteurs : Wenkai Liu, Auteur ; Qiliang Liu, Auteur ; Jie Yang, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : pp 767 - 787 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse bivariée [Termes IGN] analyse de groupement [Termes IGN] Chine [Termes IGN] hétérogénéité spatiale [Termes IGN] méthode de Monte-Carlo [Termes IGN] origine - destination [Termes IGN] réseau routier Résumé : (auteur) For bivariate origin-destination (OD) movement data composed of two types of individual OD movements, a bivariate cluster can be defined as a group of two types of OD movements, at least one of which has a high density. The identification of such bivariate clusters can provide new insights into the spatial interactions between different movement patterns. Because of spatial heterogeneity, the effective detection of inhomogeneous and irregularly shaped bivariate clusters from bivariate OD movement data remains a challenge. To fill this gap, we propose a network-constrained method for clustering two types of individual OD movements on road networks. To adaptively estimate the densities of inhomogeneous OD movements, we first define a new network-constrained density based on the concept of the shared nearest neighbor. A fast Monte Carlo simulation method is then developed to statistically estimate the density threshold for each type of OD movements. Finally, bivariate clusters are constructed using the density-connectivity mechanism. Experiments on simulated datasets demonstrate that the proposed method outperformed three state-of-the-art methods in identifying inhomogeneous and irregularly shaped bivariate clusters. The proposed method was applied to taxi and ride-hailing service datasets in Xiamen. The identified bivariate clusters successfully reveal competition patterns between taxi and ride-hailing services. Numéro de notice : A2023-206 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/13658816.2022.2137879 Date de publication en ligne : 25/10/2022 En ligne : https://doi.org/10.1080/13658816.2022.2137879 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=103108 [article]

Correlation of road network structure and urban mobility intensity: An exploratory study using geo-tagged tweets / Li Geng in ISPRS International journal of geo-information, vol 12 n° 1 (January 2023)  

Public [article]  inISPRS International journal of geo-information > vol 12 n° 1 (January 2023) . - n° 7 Titre : Correlation of road network structure and urban mobility intensity: An exploratory study using geo-tagged tweets Type de document : Article/Communication Auteurs : Li Geng, Auteur ; Ke Zhang, Auteur Année de publication : 2023 Article en page(s) : n° 7 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] corrélation automatique de points homologues [Termes IGN] données issues des réseaux sociaux [Termes IGN] Etats-Unis [Termes IGN] géobalise [Termes IGN] mobilité urbaine [Termes IGN] OpenStreetMap [Termes IGN] réseau routier [Termes IGN] Twitter Résumé : (auteur) Urban planners have been long interested in understanding how urban structure and activities are mutually influenced. Human mobility and economic activities naturally drive the formation of road network structure and the accessibility of the latter shapes the patterns of movement flow across urban space. In this paper, we perform an exploratory study on the relationship between the street network structure and the intensity of human movement in urban areas. We focus on two cities and we utilize a dataset of geo-tagged tweets that can form a proxy to urban mobility and the corresponding street networks as obtained from OpenStreetMap. We apply three network centrality measures, including closeness, betweenness and straightness centrality, calculated at a global or local scale, as well as under mixed or individual transportation mode (e.g., driving, biking and walking) with its directional accessibility, to uncover the structural properties of urban street networks. We further design an urban area transition network and apply PageRank to capture the intensity of human mobility. Our correlation analysis indicates different centrality metrics have different levels of correlation with the intensity of human movement. The closeness centrality consistently shows the highest correlation (with a coefficient around 0.6) with human movement intensity when calculated at a global scale, while straightness centrality often shows no correlation at the global scale or weaker correlation ρ≈0.4 at the local scale. The correlation levels further depend on the type of directional accessibility and of various types of transportation modes. Hence, the directionality and transportation mode, largely ignored in the analysis of road networks, are crucial. Furthermore, the strength of the correlation varies in the two cities examined, indicating potential differences in urban spatial structure and human mobility patterns. Numéro de notice : A2023-105 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueNat DOI : 10.3390/ijgi12010007 Date de publication en ligne : 28/12/2022 En ligne : https://doi.org/10.3390/ijgi12010007 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102433 [article]

Improvement of 3D LiDAR point cloud classification of urban road environment based on random forest classifier / Mahmoud Mohamed in Geocarto international, vol 38 n° inconnu ([01/01/2023])  

Public [article]  inGeocarto international > vol 38 n° inconnu [01/01/2023] Titre : Improvement of 3D LiDAR point cloud classification of urban road environment based on random forest classifier Type de document : Article/Communication Auteurs : Mahmoud Mohamed, Auteur ; Salem Morsy, Auteur ; Adel El-Shazly, Auteur Année de publication : 2023 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] réseau routier [Termes IGN] semis de points [Termes IGN] zone urbaine Mots-clés libres : cylindrical neighbourhood = voisinage cylindrique Résumé : (auteur) 3D road mapping is essential for intelligent transportation system in smart cities. Road environment receives its data from mobile laser scanning (MLS) systems in the format of LiDAR point clouds, which are distinguished with their accuracy and high density. In this paper, a mobile LiDAR data classification method based on machine learning (ML) is presented. First, data subsampling and slicing are applied, followed by cylindrical neighbourhood selection method to determine the neighbourhood of each point. Second, a new LiDAR-based point feature namely Zmodis introduced, and used along with existing fifteen geometric features as input for a ML algorithm. Finally, Random Forest classifier is applied to a part of (Paris-Lille-3D) MLS point clouds belonging to NPM3D Benchmark. The dataset is about 1.5 km long road in Lille, France with more than 98 million points. The use of Zmod improved the accuracy from 90.26% to 95.23% and achieved sufficient results for classes with low points' portion in the dataset. In addition, the Zmod is the third important feature in the classification process among the sixteen features with about 14.63%. Moreover, using the first six important features achieved almost the maximum overall accuracy with about 60% reduction in the processing time. Numéro de notice : A2022-622 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2022.2102218 Date de publication en ligne : 21/07/2022 En ligne : https://doi.org/10.1080/10106049.2022.2102218 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101357 [article]

Semi-automated Pipeline to Produce Customizable Tactile Maps of Street Intersections for People with Visual Impairments / Yuhao Jiang (2023)  

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Semantic segmentation of bridge components and road infrastructure from mobile LiDAR data / Yi-Chun Lin in ISPRS Open Journal of Photogrammetry and Remote Sensing, vol 6 (December 2022)  

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An unsupervised framework for extracting multilane roads from OpenStreetMap / Kunkun Wu in International journal of geographical information science IJGIS, vol 36 n° 11 (November 2022)  

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Evaluation of automatic prediction of small horizontal curve attributes of mountain roads in GIS environments / Sercan Gülci in ISPRS International journal of geo-information, vol 11 n° 11 (November 2022)  

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A joint deep learning network of point clouds and multiple views for roadside object classification from lidar point clouds / Lina Fang in ISPRS Journal of photogrammetry and remote sensing, vol 193 (November 2022)  

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Application of a graph convolutional network with visual and semantic features to classify urban scenes / Yongyang Xu in International journal of geographical information science IJGIS, vol 36 n° 10 (October 2022)  

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Incremental road network update method with trajectory data and UAV remote sensing imagery / Jianxin Qin in ISPRS International journal of geo-information, vol 11 n° 10 (October 2022)  

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Spatio-temporal graph convolutional networks for road network inundation status prediction during urban flooding / Faxi Yuan in Computers, Environment and Urban Systems, vol 97 (October 2022)  

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3D LiDAR aided GNSS/INS integration fault detection, localization and integrity assessment in urban canyons / Zhipeng Wang in Remote sensing, vol 14 n° 18 (September-2 2022)  

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A map matching-based method for electric vehicle charging station placement at directional road segment level / Zhoulin Yu in Sustainable Cities and Society, vol 84 (September 2022)  

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