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Geodesic geometry on graphs / Daniel Cizma in Discrete & computational geometry, vol 68 n° 1 (July 2022)
[article]
Titre : Geodesic geometry on graphs Type de document : Article/Communication Auteurs : Daniel Cizma, Auteur ; Nati Linial, Auteur Année de publication : 2022 Article en page(s) : pp 298 - 347 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie
[Termes IGN] géodésie mathématique
[Termes IGN] graphe
[Termes IGN] noeudRésumé : (auteur) We investigate a graph theoretic analog of geodesic geometry. In a graph G=(V,E) we consider a system of paths P={Pu,v:u,v∈V} where Pu,v connects vertices u and v. This system is consistent in that if vertices y, z are in Pu,v, then the subpath of Pu,v between them coincides with Py,z. A map w:E→(0,∞) is said to induce P if for every u,v∈V the path Pu,v is w-geodesic. We say that G is metrizable if every consistent path system is induced by some such w. As we show, metrizable graphs are very rare, whereas there exist infinitely many 2-connected metrizable graphs. Numéro de notice : A2022-450 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE/MATHEMATIQUE/POSITIONNEMENT Nature : Article DOI : 10.1007/s00454-021-00345-w Date de publication en ligne : 26/01/2022 En ligne : http://dx.doi.org/10.1007/s00454-021-00345-w Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100832
in Discrete & computational geometry > vol 68 n° 1 (July 2022) . - pp 298 - 347[article]Constraint-based evaluation of map images generalized by deep learning / Azelle Courtial in Journal of Geovisualization and Spatial Analysis, vol 6 n° 1 (June 2022)
[article]
Titre : Constraint-based evaluation of map images generalized by deep learning Type de document : Article/Communication Auteurs : Azelle Courtial , Auteur ; Guillaume Touya , Auteur ; Xiang Zhang, Auteur Année de publication : 2022 Projets : 2-Pas d'info accessible - article non ouvert / Article en page(s) : n° 13 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] apprentissage profond
[Termes IGN] connexité (graphes)
[Termes IGN] données d'entrainement (apprentissage automatique)
[Termes IGN] montagne
[Termes IGN] programmation par contraintes
[Termes IGN] qualité des données
[Termes IGN] rendu réaliste
[Termes IGN] route
[Vedettes matières IGN] GénéralisationRésumé : (auteur) Deep learning techniques have recently been experimented for map generalization. Although promising, these experiments raise new problems regarding the evaluation of the output images. Traditional map generalization evaluation cannot directly be applied to the results in a raster format. Additionally, the internal evaluation used by deep learning models is mostly based on the realism of images and the accuracy of pixels, and none of these criteria is sufficient to evaluate a generalization process. Finally, deep learning processes tend to hide the causal mechanisms and do not always guarantee a result that follows cartographic principles. In this article, we propose a method to adapt constraint-based evaluation to the images generated by deep learning models. We focus on the use case of mountain road generalization, and detail seven raster-based constraints, namely, clutter, coalescence reduction, smoothness, position preservation, road connectivity preservation, noise absence, and color realism constraints. These constraints can contribute to current studies on deep learning-based map generalization, as they can help guide the learning process, compare different models, validate these models, and identify remaining problems in the output images. They can also be used to assess the quality of training examples. Numéro de notice : A2022-449 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s41651-022-00104-2 Date de publication en ligne : 07/05/2022 En ligne : http://dx.doi.org/10.1007/s41651-022-00104-2 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100646
in Journal of Geovisualization and Spatial Analysis > vol 6 n° 1 (June 2022) . - n° 13[article]Context-aware network for semantic segmentation toward large-scale point clouds in urban environments / Chun Liu in IEEE Transactions on geoscience and remote sensing, vol 60 n° 6 (June 2022)
[article]
Titre : Context-aware network for semantic segmentation toward large-scale point clouds in urban environments Type de document : Article/Communication Auteurs : Chun Liu, Auteur ; Doudou Zeng, Auteur ; Akram Akbar, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 5703915 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] agrégation de détails
[Termes IGN] apprentissage profond
[Termes IGN] attention (apprentissage automatique)
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] graphe
[Termes IGN] prise en compte du contexte
[Termes IGN] segmentation sémantique
[Termes IGN] semis de points
[Termes IGN] zone urbaineRésumé : (auteur) Point cloud semantic segmentation in urban scenes plays a vital role in intelligent city modeling, autonomous driving, and urban planning. Point cloud semantic segmentation based on deep learning methods has achieved significant improvement. However, it is also challenging for accurate semantic segmentation in large scenes due to complex elements, variety of scene classes, occlusions, and noise. Besides, most methods need to split the original point cloud into multiple blocks before processing and cannot directly deal with the point clouds on a large scale. We propose a novel context-aware network (CAN) that can directly deal with large-scale point clouds. In the proposed network, a local feature aggregation module (LFAM) is designed to preserve rich geometric details in the raw point cloud and reduce the information loss during feature extraction. Then, in combination with a global context aggregation module (GCAM), capture long-range dependencies to enhance the network feature representation and suppress the noise. Finally, a context-aware upsampling module (CAUM) is embedded into the proposed network to capture the global perception from a broad perspective. The ensemble of low-level and high-level features facilitates the effectiveness and efficiency of 3-D point cloud feature refinement. Comprehensive experiments were carried out on three large-scale point cloud datasets in both outdoor and indoor environments to evaluate the performance of the proposed network. The results show that the proposed method outperformed the state-of-the-art representative semantic segmentation networks, and the overall accuracy (OA) of Tongji-3D, Semantic3D, and Stanford large-scale 3-D indoor spaces (S3DIS) is 96.01%, 95.0%, and 88.55%, respectively. Numéro de notice : A2022-561 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2022.3182776 Date de publication en ligne : 13/06/2022 En ligne : https://doi.org/10.1109/TGRS.2022.3182776 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101188
in IEEE Transactions on geoscience and remote sensing > vol 60 n° 6 (June 2022) . - n° 5703915[article]Coupling graph deep learning and spatial-temporal influence of built environment for short-term bus travel demand prediction / Tianhong Zhao in Computers, Environment and Urban Systems, vol 94 (June 2022)
[article]
Titre : Coupling graph deep learning and spatial-temporal influence of built environment for short-term bus travel demand prediction Type de document : Article/Communication Auteurs : Tianhong Zhao, Auteur ; Zhengdong Huang, Auteur ; Wei Tu, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 101776 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] apprentissage profond
[Termes IGN] bati
[Termes IGN] données spatiotemporelles
[Termes IGN] gestion de trafic
[Termes IGN] graphe
[Termes IGN] logement
[Termes IGN] migration pendulaire
[Termes IGN] modèle de simulation
[Termes IGN] régression géographiquement pondérée
[Termes IGN] service public
[Termes IGN] Shenzhen
[Termes IGN] système de transport intelligent
[Termes IGN] transport public
[Termes IGN] transport urbainRésumé : (auteur) Accurate and robust short-term bus travel prediction facilitates operating the bus fleet to provide comfortable and flexible bus services. The built environment, including land use, buildings, and public facilities, has an important influence on bus travel demand prediction. However, previous studies regarded the built environment as a static feature thus even ignored its influence on bus travel in deep learning framework. To fill this gap, we propose a graph deep learning-based approach coupling with spatiotemporal influence of built environment (GDLBE) to enhance short-term bus travel demand prediction. A time-dependent geographically weighted regression method is used to resolve the dynamic influence of the built environment on bus travel demand at different times of the day. A graph deep learning module is used to capture the comprehensive spatial and temporal dependency behind massive bus travel demand. The short-term bus travel demand is predicted by fusing the dynamic built environment influences and spatiotemporal dependency. An experiment in Shenzhen is conducted to evaluate the performance of the proposed approach. Baseline methods are compared, and the results demonstrate that the proposed approach outperforms the baselines. These results will help bus fleet dispatch for smart transportation. Numéro de notice : A2022-245 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1016/j.compenvurbsys.2022.101776 Date de publication en ligne : 12/03/2022 En ligne : https://doi.org/10.1016/j.compenvurbsys.2022.101776 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100185
in Computers, Environment and Urban Systems > vol 94 (June 2022) . - n° 101776[article]Detecting interchanges in road networks using a graph convolutional network approach / Min Yang in International journal of geographical information science IJGIS, vol 36 n° 6 (June 2022)
[article]
Titre : Detecting interchanges in road networks using a graph convolutional network approach Type de document : Article/Communication Auteurs : Min Yang, Auteur ; Chenjun Jiang, Auteur ; Xiongfeng Yan, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 1119 - 1139 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] analyse de groupement
[Termes IGN] analyse vectorielle
[Termes IGN] apprentissage profond
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] classification semi-dirigée
[Termes IGN] détection d'objet
[Termes IGN] échangeur routier
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] modélisation
[Termes IGN] noeud
[Termes IGN] Pékin (Chine)
[Termes IGN] réseau neuronal de graphes
[Termes IGN] réseau routier
[Termes IGN] Wuhan (Chine)Résumé : (auteur) Detecting interchanges in road networks benefit many applications, such as vehicle navigation and map generalization. Traditional approaches use manually defined rules based on geometric, topological, or both properties, and thus can present challenges for structurally complex interchange. To overcome this drawback, we propose a graph-based deep learning approach for interchange detection. First, we model the road network as a graph in which the nodes represent road segments, and the edges represent their connections. The proposed approach computes the shape measures and contextual properties of individual road segments for features characterizing the associated nodes in the graph. Next, a semi-supervised approach uses these features and limited labeled interchanges to train a graph convolutional network that classifies these road segments into an interchange and non-interchange segments. Finally, an adaptive clustering approach groups the detected interchange segments into interchanges. Our experiment with the road networks of Beijing and Wuhan achieved a classification accuracy >95% at a label rate of 10%. Moreover, the interchange detection precision and recall were 79.6 and 75.7% on the Beijing dataset and 80.6 and 74.8% on the Wuhan dataset, respectively, which were 18.3–36.1 and 17.4–19.4% higher than those of the existing approaches based on characteristic node clustering. Numéro de notice : A2022-404 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2021.2024195 Date de publication en ligne : 11/03/2022 En ligne : https://doi.org/10.1080/13658816.2021.2024195 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100716
in International journal of geographical information science IJGIS > vol 36 n° 6 (June 2022) . - pp 1119 - 1139[article]Graph-based block-level urban change detection using Sentinel-2 time series / Nan Wang in Remote sensing of environment, vol 274 (June 2022)PermalinkInvariant structure representation for remote sensing object detection based on graph modeling / Zicong Zhu in IEEE Transactions on geoscience and remote sensing, vol 60 n° 6 (June 2022)PermalinkTrue orthophoto generation based on unmanned aerial vehicle images using reconstructed edge points / Mojdeh Ebrahimikia in Photogrammetric record, vol 37 n° 178 (June 2022)PermalinkCity3D: Large-scale building reconstruction from airborne LiDAR point clouds / Jin Huang in Remote sensing, vol 14 n° 9 (May-1 2022)PermalinkNavigation network derivation for QR code-based indoor pedestrian path planning / Jinjin Yan in Transactions in GIS, vol 26 n° 3 (May 2022)PermalinkClustering with implicit constraints: A novel approach to housing market segmentation / Xiaoqi Zhang in Transactions in GIS, vol 26 n° 2 (April 2022)PermalinkA graph attention network for road marking classification from mobile LiDAR point clouds / Lina Fang in International journal of applied Earth observation and geoinformation, vol 108 (April 2022)PermalinkGraph learning based on signal smoothness representation for homogeneous and heterogeneous change detection / David Alejandro Jimenez-Sierra in IEEE Transactions on geoscience and remote sensing, vol 60 n° 4 (April 2022)PermalinkA cost-effective method for reconstructing city-building 3D models from sparse Lidar point clouds / Marek Kulawiak in Remote sensing, vol 14 n° 5 (March-1 2022)PermalinkA search step optimization in an ambiguity function-based GNSS precise positioning / Sławomir Cellmer in Survey review, vol 54 n° 383 (March 2022)PermalinkA robust nonrigid point set registration framework based on global and intrinsic topological constraints / Guiqiang Yang in The Visual Computer, vol 38 n° 2 (February 2022)PermalinkSNN_flow: a shared nearest-neighbor-based clustering method for inhomogeneous origin-destination flows / Qiliang Liu in International journal of geographical information science IJGIS, vol 36 n° 2 (February 2022)PermalinkUsing vertices of a triangular irregular network to calculate slope and aspect / Guanghui Hu in International journal of geographical information science IJGIS, vol 36 n° 2 (February 2022)PermalinkPermalinkTowards expressive graph neural networks : Theory, algorithms, and applications / Georgios Dasoulas (2022)PermalinkDetection of periodic displacements of shell structures with edges using spline surfaces, meshes and point clouds / Grzegorz Lenda in Reports on geodesy and geoinformatics, vol 112 n° 1 (December 2021)PermalinkA topology-based graph data model for indoor spatial-social networking / Mahdi Rahimi in International journal of geographical information science IJGIS, vol 35 n° 12 (December 2021)PermalinkBinary space partitioning visibility tree for polygonal and environment light rendering / Hiroki Okuno in The Visual Computer, vol 37 n° 9 - 11 (September 2021)PermalinkSpatial patterns of living and dead small trees in subalpine Norway spruce forest reserves in Switzerland / Eva Bianchi in Forest ecology and management, vol 494 (August-15 2021)PermalinkA typification method for linear building groups based on stroke simplification / Xiao Wang in Geocarto international, vol 36 n° 15 ([15/08/2021])Permalink