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Landscape metrics regularly outperform other traditionally-used ancillary datasets in dasymetric mapping of population / Heng Wan in Computers, Environment and Urban Systems, vol 99 (January 2023)
[article]
Titre : Landscape metrics regularly outperform other traditionally-used ancillary datasets in dasymetric mapping of population Type de document : Article/Communication Auteurs : Heng Wan, Auteur ; Jim Yoon, Auteur ; Vivek Srikrishnan, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : n° 101899 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] carte thématique
[Termes IGN] densité de population
[Termes IGN] distribution spatiale
[Termes IGN] Etats-Unis
[Termes IGN] indicateur paysager
[Termes IGN] interpolation
[Termes IGN] occupation du sol
[Termes IGN] paysage
[Termes IGN] planification urbaine
[Termes IGN] réduction d'échelleRésumé : (auteur) Population downscaling and interpolation methods are required to produce data which correspond to spatial units used in urban planning, demography, and environmental modeling. Population data are typically aggregated at census enumeration units, which can have arbitrary, temporally-evolving boundaries. Previous approaches to imperviousness-based dasymetric mapping ignore cell-level patterning of imperviousness within a spatial unit of prediction, which potentially serve as a strong indicator of population. Landscape metrics derived from imperviousness data offer a promising approach to capture these patterns. In this study, we incorporate landscape metrics derived from impervious cover percentage maps into intelligent dasymetric mapping to downscale population from census tracts to block groups in four states with varying population densities: Connecticut, South Carolina, West Virginia, and New Mexico. We compare the performance of the landscape metrics-based models against two baseline models in all four states across three different time periods. The results show that intelligent dasymetric mapping using landscape metrics generally outperforms the two baseline models. We further compare the performance of landscape metrics as an ancillary source of information for dasymetric mapping against other traditionally-used datasets (e.g., land use, roads, nighttime lights data) in three states (Connecticut, South Carolina, and New Mexico) in 2000. We find that class area, landscape shape index, and number of patches consistently achieve lower error rates than other ancillary datasets in all the three states. Numéro de notice : A2023-013 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1016/j.compenvurbsys.2022.101899 Date de publication en ligne : 02/11/2022 En ligne : https://doi.org/10.1016/j.compenvurbsys.2022.101899 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102130
in Computers, Environment and Urban Systems > vol 99 (January 2023) . - n° 101899[article]
Titre : Learnable Earth Parser: Discovering 3D Prototypes in Aerial Scans Type de document : Article/Communication Auteurs : Romain Loiseau , Auteur ; Elliot Vincent, Auteur ; Mathieu Aubry, Auteur ; Loïc Landrieu , Auteur Editeur : Ithaca [New York - Etats-Unis] : ArXiv - Université Cornell Année de publication : 2023 Importance : 18 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] information complexe
[Termes IGN] scène 3D
[Termes IGN] semis de points
[Termes IGN] zone urbaineRésumé : (auteur) We propose an unsupervised method for parsing large 3D scans of real-world scenes into interpretable parts. Our goal is to provide a practical tool for analyzing 3D scenes with unique characteristics in the context of aerial surveying and mapping, without relying on application-specific user annotations. Our approach is based on a probabilistic reconstruction model that decomposes an input 3D point cloud into a small set of learned prototypical shapes. Our model provides an interpretable reconstruction of complex scenes and leads to relevant instance and semantic segmentations. To demonstrate the usefulness of our results, we introduce a novel dataset of seven diverse aerial LiDAR scans. We show that our method outperforms state-of-the-art unsupervised methods in terms of decomposition accuracy while remaining visually interpretable. Our method offers significant advantage over existing approaches, as it does not require any manual annotations, making it a practical and efficient tool for 3D scene analysis. Our code and dataset are available at https://imagine.enpc.fr/~loiseaur/learnable-earth-parser Numéro de notice : P2023-005 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : IMAGERIE/INFORMATIQUE Nature : Preprint nature-HAL : Préprint DOI : sans En ligne : https://hal.science/hal-04135416 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=103347 Measuring metro accessibility: An exploratory study of Wuhan based on multi-source urban data / Tao Wu in ISPRS International journal of geo-information, vol 12 n° 1 (January 2023)
[article]
Titre : Measuring metro accessibility: An exploratory study of Wuhan based on multi-source urban data Type de document : Article/Communication Auteurs : Tao Wu, Auteur ; Mingjing Li, Auteur ; Ye Zhou, Auteur Année de publication : 2023 Article en page(s) : n° 18 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] accessibilité
[Termes IGN] analyse de groupement
[Termes IGN] classification par nuées dynamiques
[Termes IGN] données multisources
[Termes IGN] OpenStreetMap
[Termes IGN] planification urbaine
[Termes IGN] point d'intérêt
[Termes IGN] processus de hiérarchisation analytique
[Termes IGN] transport public
[Termes IGN] utilisation du sol
[Termes IGN] Wuhan (Chine)Résumé : (auteur) Metro accessibility has attracted interest in sustainable transport analyses. Hence, the accuracy of metro-accessibility measures have become increasingly vital. Various spatiotemporal factors, including by-metro accessibility, land-use accessibility and to-metro accessibility, affect metro accessibility; however, measuring metro accessibility while considering all these components simultaneously is challenging. By integrating these factors into a unified analysis framework, this study aims to strengthen the method for metro-accessibility assessment. Specifically, we proposed the “By metro–Land use–To metro” model to conduct a metro-accessibility index and develop an accessibility-based station typology. The results show that Wuhan metro system accessibility presented a “high-medium-low” spatial disparity from the urban center to the periphery. Meanwhile, the variety of metro-accessibility characteristics and typologies in Wuhan will equip urban planners and policymakers with a useful tool for better organising by-metro accessibility, land-use accessibility and to-metro accessibility. Numéro de notice : A2023-104 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi12010018 Date de publication en ligne : 10/01/2023 En ligne : https://doi.org/10.3390/ijgi12010018 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102432
in ISPRS International journal of geo-information > vol 12 n° 1 (January 2023) . - n° 18[article]Modèles et outils pour la publication de métadonnées d'archives géographiques et de leurs données dérivées / Melvin Hersent (2023)
Titre : Modèles et outils pour la publication de métadonnées d'archives géographiques et de leurs données dérivées Type de document : Article/Communication Auteurs : Melvin Hersent, Auteur ; Nathalie Abadie , Auteur ; Bertrand Duménieu , Auteur ; Julien Perret , Auteur Editeur : Paris : HAL Année de publication : 2023 Projets : SODUCO / Perret, Julien Conférence : Humanistica 2023, 4e conférence de l'association francophone des humanités numériques 26/06/2023 28/06/2023 Genève Suisse OA Proceedings Importance : 7 p. Format : 21 x 30 cm Note générale : bibliographie Langues : Français (fre) Descripteur : [Vedettes matières IGN] Géomatique web
[Termes IGN] échange dynamique de données
[Termes IGN] interopérabilité sémantique
[Termes IGN] métadonnées
[Termes IGN] métadonnées géographiques
[Termes IGN] norme ISO
[Termes IGN] terminologieIndex. décimale : 37.50 Géomatique web Résumé : (auteur) L'interopérabilité des données dans un projet pluridisciplinaire est primordiale. Prenant l'exemple d'un projet de recherche en histoire spatiale, nous comparerons dans un premier temps les standards et vocabulaires à notre disposition pour décrire des données géographiques et des documents d'archives. Nous proposons ensuite un alignement entre les standards retenus : l'ISO 19115 et RiC-O. Enfin, nous proposons une architecture de microservices pour la saisie, le stockage, la publication sur le Web et l'interrogation unifiée des métadonnées de nos sources. Numéro de notice : C2023-005 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : GEOMATIQUE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : sans En ligne : https://hal.science/hal-04110787 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=103274 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]A real-time algorithm for continuous navigation in intelligent transportation systems using LiDAR-Gyroscope-Odometer integration / Tarek Hassan in Journal of applied geodesy, vol 17 n° 1 (January 2023)PermalinkSemi-automated Pipeline to Produce Customizable Tactile Maps of Street Intersections for People with Visual Impairments / Yuhao Jiang (2023)PermalinkSensing urban soundscapes from street view imagery / Tianhong Zhao in Computers, Environment and Urban Systems, vol 99 (January 2023)PermalinkThe cellular automata approach in dynamic modelling of land use change detection and future simulations based on remote sensing data in Lahore Pakistan / Muhammad Nasar Ahmad in Photogrammetric Engineering & Remote Sensing, PERS, vol 89 n° 1 (January 2023)PermalinkUrban infrastructure expansion and artificial light pollution degrade coastal ecosystems, increasing natural-to-urban structural connectivity / Moisés A. Aguilera in Landscape and Urban Planning, vol 229 (January 2023)PermalinkVers une optimisation de la diffusion de l’information dans une ville intelligente / Malika Grim-Yefsah (2023)PermalinkAutomatic detection of suspected sewage discharge from coastal outfalls based on Sentinel-2 imagery / Yuxin Wang in Science of the total environment, vol 853 (December 2022)PermalinkEco-environment and coupling coordination and quantification of urbanization in Yangtze River delta considering spatial non-stationarity / Yaqiu Zhang in Geocarto international, vol 37 n° 27 ([20/12/2022])PermalinkA data-driven framework to manage uncertainty due to limited transferability in urban growth models / Jingyan Yu in Computers, Environment and Urban Systems, vol 98 (December 2022)PermalinkExtracting built-up land area of airports in China using Sentinel-2 imagery through deep learning / Fanxuan Zeng in Geocarto international, vol 37 n° 25 ([01/12/2022])Permalink