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Interactive visual analytics of moving passenger flocks using massive smart card data / Tong Zhang in Cartography and Geographic Information Science, Vol 49 n° 4 (July 2022)  

Public [article]  inCartography and Geographic Information Science > Vol 49 n° 4 (July 2022) . - pp 354 - 369 Titre : Interactive visual analytics of moving passenger flocks using massive smart card data Type de document : Article/Communication Auteurs : Tong Zhang, Auteur ; Wei He, Auteur ; Jing Huang, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 354 - 369 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] analyse spatiale [Termes IGN] analyse visuelle [Termes IGN] carte à puce [Termes IGN] données massives [Termes IGN] mobilité urbaine [Termes IGN] objet mobile [Termes IGN] Shenzhen [Termes IGN] trajet (mobilité) [Vedettes matières IGN] Géovisualisation Résumé : (auteur) Understanding urban mobility patterns is constrained by our limited capabilities to extract and visualize spatio-temporal regularities from large amounts of mobility data. Moving flocks, defined as groups of people traveling along over a pre-defined time duration, can reveal collective moving patterns at aggregated spatio-temporal scales, thereby facilitating the discovery of urban mobility structure and travel demand patterns. In this study, we extend classical trajectory-oriented flock mining algorithms to discover moving flocks of transit passengers, accounting for the constraints of multi-modal transit networks. We develop a map-centered visual analytics approach by integrating the flock mining algorithm with interactive visualization designs of discovered flocks. Novel interactive visualizations are designed and implemented to support the exploration and analyses of discovered moving flocks at different spatial and temporal scales. The visual analytics approach is evaluated using a real-world smart card dataset collected in Shenzhen City, China, validating its applicability in capturing and mapping dynamic mobility patterns over a large metropolitan area. Numéro de notice : A2022-480 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/15230406.2022.2039775 Date de publication en ligne : 09/03/2022 En ligne : https://doi.org/10.1080/15230406.2022.2039775 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100886 [article]

Modeling human–human interaction with attention-based high-order GCN for trajectory prediction / Yanyan Fang in The Visual Computer, vol 38 n° 7 (July 2022)  

Public [article]  inThe Visual Computer > vol 38 n° 7 (July 2022) . - pp 2257 - 2269 Titre : Modeling human–human interaction with attention-based high-order GCN for trajectory prediction Type de document : Article/Communication Auteurs : Yanyan Fang, Auteur ; Zhiyu Jin, Auteur ; Zhenhua Cui, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 2257 - 2269 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] attention (apprentissage automatique) [Termes IGN] détection de cible [Termes IGN] données d'entrainement (apprentissage automatique) [Termes IGN] interaction spatiale [Termes IGN] modèle de simulation [Termes IGN] objet mobile [Termes IGN] piéton [Termes IGN] réseau neuronal de graphes [Termes IGN] trajet (mobilité) Résumé : (auteur) This paper presents a novel high-order graph convolutional network (GCN) for pedestrian trajectory prediction. Specifically, the walking state of a target pedestrian depends on both its historical trajectory, which encodes its speed, walking direction and acceleration information, as well as the movement of its neighbors. Thus we propose to leverage GCNs to aggregate the trajectory features of the target pedestrian and its neighbors to predict the movement of the target pedestrian. Considering that the movement of the neighbors’ neighbors affects the movement of the target pedestrian’s neighbors, thus indirectly affecting the movement of the target pedestrian, we propose to use a high-order GCN for human–human interaction modelling. Such a high-order GCN considers the target pedestrian’s neighbors as well as its neighbors’ neighbors. Further, a pedestrian avoids collision with others by estimating its locations and its neighbors’ upcoming locations, and it slows down or changes direction if it believes a collision may occur, especially in very crowded scenes. In light of this, we propose to model such anticipation-based decision making behavior as attention and combine it with our high-order GCN. Thus we first roughly estimate the future trajectories of all pedestrians with a simple method. By using the coarse predicted future trajectory and GCN outputs, we calculate the attention in our attention-based high-order GCN and predict future trajectory. Extensive experiments validate the effectiveness of our approach. In addition, our model shows a higher data efficiency. On the ETH&UCY dataset, using only 5% of the training data for each training epoch, our model outperforms the state of the art. Numéro de notice : A2022-507 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007/s00371-021-02109-2 Date de publication en ligne : 01/07/2021 En ligne : https://doi.org/10.1007/s00371-021-02109-2 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101040 [article]

Street-view imagery guided street furniture inventory from mobile laser scanning point clouds / Yuzhou Zhou in ISPRS Journal of photogrammetry and remote sensing, vol 189 (July 2022)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 189 (July 2022) . - pp 63 - 77 Titre : Street-view imagery guided street furniture inventory from mobile laser scanning point clouds Type de document : Article/Communication Auteurs : Yuzhou Zhou, Auteur ; Xu Han, Auteur ; Mingjun Peng, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 63 - 77 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] détection d'objet [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] image Streetview [Termes IGN] instance [Termes IGN] inventaire [Termes IGN] jeu de données localisées [Termes IGN] masque [Termes IGN] mobilier urbain [Termes IGN] segmentation sémantique [Termes IGN] semis de points [Termes IGN] séparateur à vaste marge [Termes IGN] Shanghai (Chine) [Termes IGN] Wuhan (Chine) Résumé : (auteur) Outdated or sketchy inventory of street furniture may misguide the planners on the renovation and upgrade of transportation infrastructures, thus posing potential threats to traffic safety. Previous studies have taken their steps using point clouds or street-view imagery (SVI) for street furniture inventory, but there remains a gap to balance semantic richness, localization accuracy and working efficiency. Therefore, this paper proposes an effective pipeline that combines SVI and point clouds for the inventory of street furniture. The proposed pipeline encompasses three steps: (1) Off-the-shelf street furniture detection models are applied on SVI for generating two-dimensional (2D) proposals and then three-dimensional (3D) point cloud frustums are accordingly cropped; (2) The instance mask and the instance 3D bounding box are predicted for each frustum using a multi-task neural network; (3) Frustums from adjacent perspectives are associated and fused via multi-object tracking, after which the object-centric instance segmentation outputs the final street furniture with 3D locations and semantic labels. This pipeline was validated on datasets collected in Shanghai and Wuhan, producing component-level street furniture inventory of nine classes. The instance-level mean recall and precision reach 86.4%, 80.9% and 83.2%, 87.8% respectively in Shanghai and Wuhan, and the point-level mean recall, precision, weighted coverage all exceed 73.7%. Numéro de notice : A2022-403 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.isprsjprs.2022.04.023 Date de publication en ligne : 12/05/2022 En ligne : https://doi.org/10.1016/j.isprsjprs.2022.04.023 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100711 [article]

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Detecting spatiotemporal traffic events using geosocial media data / Shishuo Xu in Computers, Environment and Urban Systems, vol 94 (June 2022)  

Public [article]  inComputers, Environment and Urban Systems > vol 94 (June 2022) . - n° 101797 Titre : Detecting spatiotemporal traffic events using geosocial media data Type de document : Article/Communication Auteurs : Shishuo Xu, Auteur ; Songnian Li, Auteur ; Wei Huang, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 101797 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] analyse de groupement [Termes IGN] base de données d'objets mobiles [Termes IGN] base de données spatiotemporelles [Termes IGN] détection d'événement [Termes IGN] données issues des réseaux sociaux [Termes IGN] données spatiotemporelles [Termes IGN] planification urbaine [Termes IGN] sécurité routière [Termes IGN] Toronto [Termes IGN] trafic routier [Termes IGN] Twitter Résumé : (auteur) Social media platforms enable efficient traffic event detection by allowing users to produce geo-tagged content (e.g., tweets) known as geosocial media data. Geosocial media data improve road safety by providing timely updates for traffic flow and traffic control. Recent studies on traffic event detection with geosocial media data have been focused around keyword-based query approaches, where the event content was inferred by predetermined categories, to retrieve relevant traffic events. Spatiotemporal features associated with traffic-related posts have not been fully investigated. In this study, we filtered irrelevant posts with association rules. A spatiotemporal clustering-based method was then used to retrieve traffic events from these filtered posts, where the content of detected events was automatically inferred with a set of representative terms. For comparison, a typical text classification-based method was also used by classifying the posts filtered from association rules into different categories. By validating the detection results with vehicle travel speed data, we demonstrate that the former outperforms the latter in terms of the number of correctly detected traffic events from one-year of Twitter data in Toronto, Canada. Our proposed approach helps organizations and governments to be aware of when and where traffic events occur by identifying event hotspots and peak periods, which improves both traffic management and urban planning. Numéro de notice : A2022-264 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1016/j.compenvurbsys.2022.101797 Date de publication en ligne : 26/03/2022 En ligne : https://doi.org/10.1016/j.compenvurbsys.2022.101797 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100261 [article]

Multi-resolution representation using graph database / Yizhi Huang in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-4-2022 (2022 edition)  

Public [article]  inISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences > vol V-4-2022 (2022 edition) . - pp 173 - 180 Titre : Multi-resolution representation using graph database Type de document : Article/Communication Auteurs : Yizhi Huang, Auteur ; Emmanuel Stefanakis, Auteur Année de publication : 2022 Article en page(s) : pp 173 - 180 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] base de données de graphes [Termes IGN] interface utilisateur [Termes IGN] objet géographique [Termes IGN] représentation multiple [Termes IGN] requête spatiale Mots-clés libres : Neo4j Résumé : (auteur) Multi-resolution representation has always been an important and popular data source for many research and applications, such as navigation, land cover, map generation, media event forecasting, etc. With one spatial object represented by distinct geometries at different resolutions, multi-resolution representation is high in complexity. Most of the current approaches for storing and retrieving multi-resolution representation are either complicated in structure, or time consuming in traversal and query. In addition, supports on direct navigation between different representations are still intricate in most of the paradigms, especially in topological map sets. To address this problem, we propose a novel approach for storing, querying, and extracting multi-resolution representation. The development of this approach is based on Neo4j, a graph database platform that is famous for its powerful query and advanced flexibility. Benefited from the intuitiveness of the proposed database structure, direct navigation between representations of one spatial object, and between groups of representations at adjacent resolutions are both available. On top of this, collaborating with the self-designed web-based interface, queries within the proposed approach truly embraced the concept of keyword search, which lower the barrier between novice users and complicate queries. In all, the proposed system demonstrates the potential of managing multi-resolution representation data through the graph database and could be a time-saver for related processes. Numéro de notice : A2022-425 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Article DOI : 10.5194/isprs-annals-V-4-2022-173-2022 Date de publication en ligne : 18/05/2022 En ligne : https://doi.org/10.5194/isprs-annals-V-4-2022-173-2022 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100730 [article]

Research on machine intelligent perception of urban geographic location based on high resolution remote sensing images / Jun Chen in Photogrammetric Engineering & Remote Sensing, PERS, vol 88 n° 4 (April 2022)  

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Volunteered geographic information mobile application for participatory landslide inventory mapping / Raden Muhammad Anshori in Computers & geosciences, vol 161 (April 2022)  

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Comparaison des images satellite et aériennes dans le domaine de la détection d’obstacles à la navigation aérienne et de leur mise à jour / Olivier de Joinville in XYZ, n° 170 (mars 2022)

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Early warning of COVID-19 hotspots using human mobility and web search query data / Takahiro Yabe in Computers, Environment and Urban Systems, vol 92 (March 2022)  

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A survey on semantic question answering systems / Christina Antoniou in The Knowledge Engineering Review, vol 37 (2022)  

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Automatic structuring of photographic collections for spatio-temporal monitoring of restoration sites: problem statement and challenges / Laura Willot (2022)  

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Interactive HGIS platform union of Lublin (1569): A geomatic solution for discovering the Jagiellonian heritage of the city / Jakub Kuna in Journal of Cultural Heritage, vol 53 (January–February 2022)  

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Replication and the search for the laws in the geographic sciences / Peter Kedron in Annals of GIS, vol 28 n° 1 (January 2022)  

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Semantic segmentation of high-resolution remote sensing images based on a class feature attention mechanism fused with Deeplabv3+ / Zhimin Wang in Computers & geosciences, vol 158 (January 2022)  

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VGI3D: an interactive and low-cost solution for 3D building modelling from street-level VGI images / Chaoquan Zhang in Journal of Geovisualization and Spatial Analysis, vol 5 n° 2 (December 2021)  

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