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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)  

Public [article]  inComputers, Environment and Urban Systems > vol 97 (October 2022) . - n° 101870 Titre : Spatio-temporal graph convolutional networks for road network inundation status prediction during urban flooding Type de document : Article/Communication Auteurs : Faxi Yuan, Auteur ; Yuanchang Xu, Auteur ; Qingchun Li, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 101870 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] catastrophe naturelle [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] graphe [Termes IGN] inondation [Termes IGN] modèle de simulation [Termes IGN] polynôme de Chebysheff [Termes IGN] prévention des risques [Termes IGN] réseau neuronal de graphes [Termes IGN] réseau routier [Termes IGN] Texas (Etats-Unis) [Termes IGN] zone urbaine Résumé : (auteur) The objective of this study is to predict the near-future flooding status of road segments based on their own and adjacent road segments' current status through the use of deep learning framework on fine-grained traffic data. Predictive flood monitoring for situational awareness of road network status plays a critical role to support crisis response activities such as evaluation of the loss of access to hospitals and shelters. Existing studies related to near-future prediction of road network flooding status at road segment level are missing. Using fine-grained traffic speed data related to road sections, this study designed and implemented three spatio-temporal graph convolutional network (STGCN) models to predict road network status during flood events at the road segment level in the context of the 2017 hurricane Harvey in Harris County (Texas, USA). Model 1 consists of two spatio-temporal blocks considering the adjacency and distance between road segments, while model 2 contains an additional elevation block to account for elevation difference between road segments. Model 3 includes three blocks for considering the adjacency and the product of distance and elevation difference between road segments. The analysis tested the STGCN models and evaluated their prediction performance. Our results indicated that model 1 and model 2 have reliable and accurate performance for predicting road network flooding status in near future (e.g., 2–4 h) with model precision and recall values larger than 98% and 96%, respectively. With reliable road network status predictions in floods, the proposed model can benefit affected communities to avoid flooded roads and the emergency management agencies to implement evacuation and relief resource delivery plans. Numéro de notice : A2022-656 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1016/j.compenvurbsys.2022.101870 Date de publication en ligne : 22/08/2022 En ligne : https://doi.org/10.1016/j.compenvurbsys.2022.101870 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101506 [article]

Geodesic geometry on graphs / Daniel Cizma in Discrete & computational geometry, vol 8 n° 1 (July 2022)  

Public [article]  inDiscrete & computational geometry > vol 8 n° 1 (July 2022) . - pp 298 - 347 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] noeud Résumé : (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 [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)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 60 n° 6 (June 2022) . - n° 5703915 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 urbaine Résumé : (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 [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)  

Public [article]  inComputers, Environment and Urban Systems > vol 94 (June 2022) . - n° 101776 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 urbain Résumé : (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 [article]

Graph-based block-level urban change detection using Sentinel-2 time series / Nan Wang in Remote sensing of environment, vol 274 (June 2022)  

Public [article]  inRemote sensing of environment > vol 274 (June 2022) . - n° 112993 Titre : Graph-based block-level urban change detection using Sentinel-2 time series Type de document : Article/Communication Auteurs : Nan Wang, Auteur ; Wei Li, Auteur ; Ran Tao, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 112993 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse multivariée [Termes IGN] bâtiment [Termes IGN] Chine [Termes IGN] détection de changement [Termes IGN] espace vert [Termes IGN] extraction de traits caractéristiques [Termes IGN] graphe [Termes IGN] image Sentinel-MSI [Termes IGN] occupation du sol [Termes IGN] OpenStreetMap [Termes IGN] segmentation d'image [Termes IGN] série temporelle [Termes IGN] zone urbaine Résumé : (auteur) Remote sensing technology has been frequently used to obtain information on changes in urban land cover because of its vast spatial coverage and timeliness of observation. Block-level change detection with high temporal resolution image data provides fine detail of urban changes, is suitable for urban management, and has gradually received widespread attention. High-dimensional features are required to express the heterogeneous structure of the blocks. High-dimensional high-frequency time series, namely, multivariate time series, are formed by arranging high-dimensional features chronologically. Classic change detection methods treat multivariate time series as univariate time series one by one. Few studies have analyzed the change in a multivariate time series by considering all variables as an entirety. Therefore, a graph-based segmentation for multivariate time series algorithm (MTS-GS) is proposed in this paper. Specifically, 1) we construct a similarity matrix to explore the changing patterns of multivariate time series for seasonal change, trend change, abrupt change, and noise disturbance; 2) a multivariate time series graph is defined based on the changing patterns; and 3) the corresponding graph segmentation algorithm is proposed in the paper to detect the abrupt and trend changes under noise and seasonal disturbances. Sentinel-2 images of the rapidly developing third-tier city of Luoyang, Henan province, China, are adopted to validate the algorithm. The F1-score in the spatial domain is 84.1%; the producer's and the user's accuracy in the temporal dimension are 81.8% and 80.1%, respectively. Seven change types are defined and extracted, showing the development pattern and the efficiency of land use in the city. Furthermore, the proposed MTS-GS can be used for pixel-level change detection and performs well under various time intervals and cloud covers. Numéro de notice : A2022-399 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.rse.2022.112993 Date de publication en ligne : 16/03/2022 En ligne : https://doi.org/10.1016/j.rse.2022.112993 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100699 [article]

Invariant 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)  

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True orthophoto generation based on unmanned aerial vehicle images using reconstructed edge points / Mojdeh Ebrahimikia in Photogrammetric record, vol 37 n° 178 (June 2022)  

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Graph 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)  

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A 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)  

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Binary space partitioning visibility tree for polygonal and environment light rendering / Hiroki Okuno in The Visual Computer, vol 37 n° 9 - 11 (September 2021)  

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Constrained shortest path problems in bi-colored graphs: a label-setting approach / Amin AliAbdi in Geoinformatica [en ligne], vol 25 n° 3 (July 2021)  

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A Bayesian displacement field approach to accurate registration of SAR images / Mingtao Ding in Geocarto international, vol 36 n° 9 ([15/05/2021])  

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A new small area estimation algorithm to balance between statistical precision and scale / Cédric Vega in International journal of applied Earth observation and geoinformation, vol 97 (May 2021)  

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A BiLSTM-CNN model for predicting users’ next locations based on geotagged social media / Yi Bao in International journal of geographical information science IJGIS, vol 35 n° 4 (April 2021)  

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Graph convolutional networks by architecture search for PolSAR image classification / Hongying Liu in Remote sensing, vol 13 n° 7 (April-1 2021)  

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