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MTMGNN: Multi-time multi-graph neural network for metro passenger flow prediction / Du Yin in Geoinformatica, vol 27 n° 1 (January 2023)
[article]
Titre : MTMGNN: Multi-time multi-graph neural network for metro passenger flow prediction Type de document : Article/Communication Auteurs : Du Yin, Auteur ; Renhe Jiang, Auteur ; Jiewen Deng, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : pp 77 - 105 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] attention (apprentissage automatique)
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] déformation temporelle dynamique (algorithme)
[Termes IGN] données multitemporelles
[Termes IGN] données spatiotemporelles
[Termes IGN] flux
[Termes IGN] gestion de trafic
[Termes IGN] origine - destination
[Termes IGN] réseau neuronal de graphes
[Termes IGN] système de transport intelligent
[Termes IGN] trafic urbain
[Termes IGN] transport public
[Termes IGN] utilisateurRésumé : (auteur) The passenger flow prediction of the public metro system is a core and critical part of the intelligent transportation system, and is essential for traffic management, metro planning, and emergency safety measures. Most methods chose the recent segment from historical data as input to predict the future traffic flow; however, this would lead to the loss of the inherent characteristic information of the metro passenger flow’s daily morning and evening peak. Therefore, this study aggregates the recent-term and long-term information and use a long-term Gated Convolutional Neural Network (Gated CNN) to extract the temporal feature from the complex historical data. On the other hand, typical models did not consider the different spatial dependencies between different metro stations; this work proposes various adjacent relationships to characterize the degree of association between nodes. In order to extract spatial and temporal features at the same time, the historical data of recent-term and long-term is merged together to extract spatial features through a multi-graph neural network module. By combining Gated CNN and multi-graph module, we propose a multi-time multi-graph neural network named MTMGNN for metro passenger flow prediction. The result of our experiment on real-world datasets shows that our model MTMGNN is better than all state-of-art methods. Numéro de notice : A2023-113 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1007/s10707-022-00466-1 Date de publication en ligne : 25/04/2022 En ligne : https://doi.org/10.1007/s10707-022-00466-1 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102478
in Geoinformatica > vol 27 n° 1 (January 2023) . - pp 77 - 105[article]Multi-information PointNet++ fusion method for DEM construction from airborne LiDAR data / Hong Hu in Geocarto international, vol 38 n° 1 ([01/01/2023])
[article]
Titre : Multi-information PointNet++ fusion method for DEM construction from airborne LiDAR data Type de document : Article/Communication Auteurs : Hong Hu, Auteur ; Guanghe Zhang, Auteur ; Jianfeng Ao, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : n° 2153929 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] apprentissage profond
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] filtrage de points
[Termes IGN] image RVB
[Termes IGN] Kappa de Cohen
[Termes IGN] modèle numérique de surface
[Termes IGN] Perceptron multicouche
[Termes IGN] segmentation
[Termes IGN] semis de pointsRésumé : (auteur) Airborne light detection and ranging (LiDAR) is a popular technology in remote sensing that can significantly improve the efficiency of digital elevation model (DEM) construction. However, it is challenging to identify the real terrain features in complex areas using LiDAR data. To solve this problem, this work proposes a multi-information fusion method based on PointNet++ to improve the accuracy of DEM construction. The RGB data and normalized coordinate information of the point cloud was added to increase the number of channels on the input side of the PointNet++ neural network, which can improve the accuracy of the classification during feature extraction. Low and high density point clouds obtained from the International Society for Photogrammetry and Remote Sensing (ISPRS) and the United States Geological Survey (USGS) were used to test this proposed method. The results suggest that the proposed method improves the Kappa coefficient by 8.81% compared to PointNet++. The type I error was reduced by 2.13%, the type II error was reduced by 8.29%, and the total error was reduced by 2.52% compared to the conventional algorithm. Therefore, it is possible to conclude that the proposed method can obtain DEMs with higher accuracy. Numéro de notice : A2023-056 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1080/10106049.2022.2153929 Date de publication en ligne : 23/12/2022 En ligne : https://doi.org/10.1080/10106049.2022.2153929 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102389
in Geocarto international > vol 38 n° 1 [01/01/2023] . - n° 2153929[article]Prototype-guided multitask adversarial network for cross-domain LiDAR point clouds semantic segmentation / Zhimin Yuan in IEEE Transactions on geoscience and remote sensing, vol 61 n° 1 (January 2023)
[article]
Titre : Prototype-guided multitask adversarial network for cross-domain LiDAR point clouds semantic segmentation Type de document : Article/Communication Auteurs : Zhimin Yuan, Auteur ; Ming Cheng, Auteur ; Wankang Zeng, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : n° 5700613 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] alignement des données
[Termes IGN] apprentissage non-dirigé
[Termes IGN] compression de données
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] réseau antagoniste génératif
[Termes IGN] segmentation sémantique
[Termes IGN] semis de pointsRésumé : (auteur) Unsupervised domain adaptation (UDA) segmentation aims to leverage labeled source data to make accurate predictions on unlabeled target data. The key is to make the segmentation network learn domain-invariant representations. In this work, we propose a prototype-guided multitask adversarial network (PMAN) to achieve this. First, we propose an intensity-aware segmentation network (IAS-Net) that leverages the private intensity information of target data to substantially facilitate feature learning of the target domain. Second, the category-level cross-domain feature alignment strategy is introduced to flee the side effects of global feature alignment. It employs the prototype (class centroid) and includes two essential operations: 1) build an auxiliary nonparametric classifier to evaluate the semantic alignment degree of each point based on the prediction consistency between the main and auxiliary classifiers and 2) introduce two class-conditional point-to-prototype learning objectives for better alignment. One is to explicitly perform category-level feature alignment in a progressive manner, and the other aims to shape the source feature representation to be discriminative. Extensive experiments reveal that our PMAN outperforms state-of-the-art results on two benchmark datasets. Numéro de notice : A2023-118 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2023.3234542 Date de publication en ligne : 05/01/2023 En ligne : https://doi.org/10.1109/TGRS.2023.3234542 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102489
in IEEE Transactions on geoscience and remote sensing > vol 61 n° 1 (January 2023) . - n° 5700613[article]PSMNet-FusionX3 : LiDAR-guided deep learning stereo dense matching on aerial images / Teng Wu (2023)
Titre : PSMNet-FusionX3 : LiDAR-guided deep learning stereo dense matching on aerial images Type de document : Article/Communication Auteurs : Teng Wu , Auteur ; Bruno Vallet , Auteur ; Marc Pierrot-Deseilligny , Auteur Editeur : Computer vision foundation CVF Année de publication : 2023 Conférence : CVPR 2023, IEEE Conference on Computer Vision and Pattern Recognition workshops 18/06/2023 22/06/2023 Vancouver Colombie britannique - Canada OA Proceedings Importance : pp 6526 - 6535 Note générale : bibliographie
voir aussi https://openaccess.thecvf.com/content/CVPR2023W/PCV/supplemental/Wu_PSMNet-FusionX3_LiDAR-Guided_Deep_CVPRW_2023_supplemental.pdfLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] appariement dense
[Termes IGN] apprentissage profond
[Termes IGN] chaîne de traitement
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] image aérienne à axe vertical
[Termes IGN] scène 3D
[Termes IGN] Triangulated Irregular NetworkRésumé : (auteur) Dense image matching (DIM) and LiDAR are two complementary techniques for recovering the 3D geometry of real scenes. While DIM provides dense surfaces, they are often noisy and contaminated with outliers. Conversely, LiDAR is more accurate and robust, but less dense and more expensive compared to DIM. In this work, we investigate learning-based methods to refine surfaces produced by photogrammetry with sparse LiDAR point clouds. Unlike the current state-of-the-art approaches in the computer vision community, our focus is on aerial acquisitions typical in photogrammetry. We propose a densification pipeline that adopts a PSMNet backbone with triangulated irregular network interpolation based expansion, feature enhancement in cost volume, and conditional cost volume normalization, i.e. PSMNet-FusionX3. Our method works better on low density and is less sensitive to distribution, demonstrating its effectiveness across a range of LiDAR point cloud densities and distributions, including analyses of dataset shifts. Furthermore, we have made both our aerial (image and disparity) dataset and code available for public use. Further information can be found at https://github.com/ whuwuteng/PSMNet-FusionX3. Numéro de notice : C2023-006 Affiliation des auteurs : UGE-LASTIG (2020- ) Thématique : IMAGERIE/INFORMATIQUE Nature : Communication DOI : sans En ligne : https://openaccess.thecvf.com/content/CVPR2023W/PCV/papers/Wu_PSMNet-FusionX3_Li [...] Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=103277 Remote sensing techniques for water management and climate change monitoring in drought areas: case studies in Egypt and Tunisia / Lifan Ji in European journal of remote sensing, vol 56 n° 1 (2023)
[article]
Titre : Remote sensing techniques for water management and climate change monitoring in drought areas: case studies in Egypt and Tunisia Type de document : Article/Communication Auteurs : Lifan Ji, Auteur ; Yihao Shao, Auteur ; Jianjun Liu, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : pp 1 - 16 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse diachronique
[Termes IGN] changement climatique
[Termes IGN] Egypte
[Termes IGN] gestion de l'eau
[Termes IGN] humidité du sol
[Termes IGN] image optique
[Termes IGN] image radar moirée
[Termes IGN] image Sentinel-SAR
[Termes IGN] indice de végétation
[Termes IGN] réseau neuronal artificiel
[Termes IGN] stress hydrique
[Termes IGN] Tunisie
[Termes IGN] zone semi-arideRésumé : (auteur) This study focused on monitoring the water status of vegetation and soil by exploiting the synergy of optical and microwave satellite data with the aim of improving the knowledge of water cycle in cultivated lands in Egyptian Delta and Tunisian areas. Environmental analysis approaches based on optical and synthetic aperture radar data were carried out to set up the basis for future implementation of practical and cost-effective methods for sustainable water use in agriculture. Long-term behaviors of vegetation indices were thus analyzed between 2000 and 2018. By using SAR data from Sentinel-1, an Artificial Neural Network-based algorithm was implemented for estimating soil moisture and monthly maps for 2018 have been generated to be compared with information derived from optical indices. Moreover, a novel drought severity index was developed and applied to available data. The index was obtained by combining vegetation soil difference index, derived from optical data, and soil moisture content derived from SAR data. The proposed index was found capable of complementing optical and microwave sensitivity to drought-related parameters, although ground data are missing for correctly validating the results, by capturing drought patterns and their temporal evolution better than indices based only on microwave or optical data. Numéro de notice : A2023-103 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1080/22797254.2022.2157335 Date de publication en ligne : 06/01/2023 En ligne : https://doi.org/10.1080/22797254.2022.2157335 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102430
in European journal of remote sensing > vol 56 n° 1 (2023) . - pp 1 - 16[article]Semi-supervised label propagation for multi-source remote sensing image change detection / Fan Hao in Computers & geosciences, vol 170 (January 2023)PermalinkSensing urban soundscapes from street view imagery / Tianhong Zhao in Computers, Environment and Urban Systems, vol 99 (January 2023)PermalinkSimplified automatic prediction of the level of damage to similar buildings affected by river flood in a specific area / David Marín-García in Sustainable Cities and Society, vol 88 (January 2023)PermalinkPermalinkA survey and benchmark of automatic surface reconstruction from point clouds / Raphaël Sulzer (2023)PermalinkTree height-growth trajectory estimation using uni-temporal UAV laser scanning data and deep learning / Stefano Puliti in Forestry, an international journal of forest research, vol 96 n° 1 (January 2023)PermalinkAssessing spatio-temporal mapping and monitoring of climatic variability using SPEI and RF machine learning models / Saadia Sultan Wahlaa in Geocarto international, vol 37 n° 27 ([20/12/2022])PermalinkAbove ground biomass estimation from UAV high resolution RGB images and LiDAR data in a pine forest in Southern Italy / Mauro Maesano in iForest, biogeosciences and forestry, vol 15 n° 6 (December 2022)PermalinkA comparative study on deep-learning methods for dense image matching of multi-angle and multi-date remote sensing stereo-images / Hessah Albanwan in Photogrammetric record, vol 37 n° 180 (December 2022)PermalinkA deep learning framework based on generative adversarial networks and vision transformer for complex wetland classification using limited training samples / Ali Jamali in International journal of applied Earth observation and geoinformation, vol 115 (December 2022)Permalink