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A geospatial workflow for the assessment of public transit system performance using near real-time data / Anastassios Dardas in Transactions in GIS, vol 26 n° 4 (June 2022)
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Titre : A geospatial workflow for the assessment of public transit system performance using near real-time data Type de document : Article/Communication Auteurs : Anastassios Dardas, Auteur ; Brent Hall, Auteur ; Jon Salter, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 1642 - 1664 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG
[Termes IGN] ArcGIS
[Termes IGN] Calgary
[Termes IGN] collecte de données
[Termes IGN] données spatiotemporelles
[Termes IGN] itinéraire
[Termes IGN] planification urbaine
[Termes IGN] Python (langage de programmation)
[Termes IGN] stockage de données
[Termes IGN] temps réel
[Termes IGN] trafic routier
[Termes IGN] transport public
[Termes IGN] WebSIGRésumé : (auteur) This article presents the development of a Geographical Information Systems (GIS) workflow that harvests high-volume and high-frequency near real-time data from a public General Transit Feed Specification (GTFS) and calculates metrics for the assessment of on-time and route speed performance for a public transit system. The approach is applied to near real-time and static GTFS data collected over a 9-month period for the City of Calgary, Alberta, Canada. The workflow uses two Azure Virtual Machines (VMs), one to harvest the data and the other to process observations in parallel using Python and the ArcGIS API libraries. A Web GIS application is described that queries data from MongoDB to visualize the performance results in spatiotemporal form. The purpose of the workflow and Web GIS application is to provide actionable information to transit planners to improve public transportation systems. The data management and analysis workflow is transferable to similar GTFS data from other cities. Numéro de notice : A2022-531 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : sans Date de publication en ligne : 02/05/2022 En ligne : https://doi.org/10.1111/tgis.12942 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101078
in Transactions in GIS > vol 26 n° 4 (June 2022) . - pp 1642 - 1664[article]GIS and machine learning for analysing influencing factors of bushfires using 40-year spatio-temporal bushfire data / Wanqin He in ISPRS International journal of geo-information, vol 11 n° 6 (June 2022)
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Titre : GIS and machine learning for analysing influencing factors of bushfires using 40-year spatio-temporal bushfire data Type de document : Article/Communication Auteurs : Wanqin He, Auteur ; Sara Shirowzhan, Auteur ; Christopher Pettit, Auteur Année de publication : 2022 Article en page(s) : n° 336 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG
[Termes IGN] analyse diachronique
[Termes IGN] apprentissage automatique
[Termes IGN] brousse
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] coefficient de corrélation
[Termes IGN] données météorologiques
[Termes IGN] données spatiotemporelles
[Termes IGN] humidité du sol
[Termes IGN] incendie
[Termes IGN] indice de végétation
[Termes IGN] Nouvelle-Galles du Sud
[Termes IGN] prévention des risques
[Termes IGN] régression linéaire
[Termes IGN] Spark
[Termes IGN] système d'information géographique
[Termes IGN] température de l'airRésumé : (auteur) The causes of bushfires are extremely complex, and their scale of burning and probability of occurrence are influenced by the interaction of a variety of factors such as meteorological factors, topography, human activity and vegetation type. An in-depth understanding of the combined mechanisms of factors affecting the occurrence and spread of bushfires is needed to support the development of effective fire prevention plans and fire suppression measures and aid planning for geographic, ecological maintenance and urban emergency management. This study aimed to explore how bushfires, meteorological variability and other natural factors have interacted over the past 40 years in NSW Australia and how these influencing factors synergistically drive bushfires. The CSIRO’s Spark toolkit has been used to simulate bushfire burning spread over 24 h. The study uses NSW wildfire data from 1981–2020, combined with meteorological factors (temperature, precipitation, wind speed), vegetation data (NDVI data, vegetation type) and topography (slope, soil moisture) data to analyse the relationship between bushfires and influencing factors quantitatively. Machine learning-random forest regression was then used to determine the differences in the influence of bushfire factors on the incidence and burn scale of bushfires. Finally, the data on each influence factor was imported into Spark, and the results of the random forest model were used to set different influence weights in Spark to visualise the spread of bushfires burning over 24 h in four hotspot regions of bushfire in NSW. Wind speed, air temperature and soil moisture were found to have the most significant influence on the spread of bushfires, with the combined contribution of these three factors exceeding 60%, determining the spread of bushfires and the scale of burning. Precipitation and vegetation showed a greater influence on the annual frequency of bushfires. In addition, burn simulations show that wind direction influences the main direction of fire spread, whereas the shape of the flame front is mainly due to the influence of land classification. Besides, the simulation results from Spark could predict the temporal and spatial spread of fire, which is a potential decision aid for fireproofing agencies. The results of this study can inform how fire agencies can better understand fire occurrence mechanisms and use bushfire prediction and simulation techniques to support both their operational (short-term) and strategic (long-term) fire management responses and policies. Numéro de notice : A2022-481 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi11060336 Date de publication en ligne : 05/06/2022 En ligne : https://doi.org/10.3390/ijgi11060336 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100894
in ISPRS International journal of geo-information > vol 11 n° 6 (June 2022) . - n° 336[article]A GIS-based approach for identification of optimum runoff harvesting sites and storage estimation: a study from Subarnarekha-Kangsabati Interfluve, India / Manas Karmakar in Applied geomatics, vol 14 n° 2 (June 2022)
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Titre : A GIS-based approach for identification of optimum runoff harvesting sites and storage estimation: a study from Subarnarekha-Kangsabati Interfluve, India Type de document : Article/Communication Auteurs : Manas Karmakar, Auteur ; Debasis Ghosh, Auteur Année de publication : 2022 Article en page(s) : pp 253 - 266 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG
[Termes IGN] analyse multicritère
[Termes IGN] ArcGIS
[Termes IGN] barrage
[Termes IGN] couche thématique
[Termes IGN] eau de surface
[Termes IGN] eau pluviale
[Termes IGN] géomorphologie locale
[Termes IGN] gestion de l'eau
[Termes IGN] Inde
[Termes IGN] MNS SRTM
[Termes IGN] plan d'eau
[Termes IGN] ruissellement
[Termes IGN] stockageRésumé : (auteur) Numéro de notice : A2022-491 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1007/s12518-022-00433-3 Date de publication en ligne : 29/03/2022 En ligne : https://doi.org/10.1007/s12518-022-00433-3 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100789
in Applied geomatics > vol 14 n° 2 (June 2022) . - pp 253 - 266[article]GIS-based assessment of long-term traffic accidents using spatiotemporal and empirical Bayes analysis in Turkey / Saffet Erdoğan in Applied geomatics, vol 14 n° 2 (June 2022)
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Titre : GIS-based assessment of long-term traffic accidents using spatiotemporal and empirical Bayes analysis in Turkey Type de document : Article/Communication Auteurs : Saffet Erdoğan, Auteur ; Mehmet Ali Dereli, Auteur ; Halil İbrahim Şenol, Auteur Année de publication : 2022 Article en page(s) : pp 147 - 162 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] accident de la route
[Termes IGN] analyse de groupement
[Termes IGN] distribution spatiale
[Termes IGN] données spatiotemporelles
[Termes IGN] données statistiques
[Termes IGN] sécurité routière
[Termes IGN] système d'information géographique
[Termes IGN] théorème de Bayes
[Termes IGN] trafic routier
[Termes IGN] TurquieRésumé : (auteur) The number of traffic fatalities continues to rise steadily throughout the world. In 2016, it reached 1.35 million. The spatiotemporal analysis makes a big contribution when used with spatial and statistical analysis together in terms of the understanding of the change. This study focuses on spatiotemporal fluctuations in traffic accident hotspots to gain useful insights into traffic safety in Turkey in 2004–2017 period. For this purpose, 372,800 accident records are arranged on a GIS platform. The areas that lack traffic safety and require more attention were determined using spatial, temporal, and empirical Bayesian analysis. Although similar results were detected with spatiotemporal and empiric Bayes analysis, spatiotemporal analysis was used to understand where traffic accidents clustering, and how the trends of traffic accidents change whether are increasing or decreasing. As a result of the analysis, an increasing trend has been found in many locations in Turkey from 2004 to 2017. Numéro de notice : A2022-461 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1007/s12518-022-00419-1 Date de publication en ligne : 02/02/2022 En ligne : https://doi.org/10.1007/s12518-022-00419-1 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100788
in Applied geomatics > vol 14 n° 2 (June 2022) . - pp 147 - 162[article]HyperNet: A deep network for hyperspectral, multispectral, and panchromatic image fusion / Kun Li in ISPRS Journal of photogrammetry and remote sensing, vol 188 (June 2022)
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Titre : HyperNet: A deep network for hyperspectral, multispectral, and panchromatic image fusion Type de document : Article/Communication Auteurs : Kun Li, Auteur ; Wei Zhang, Auteur ; Dian Yu, Auteur ; Xin Tian, Auteur Année de publication : 2022 Article en page(s) : pp 30 - 44 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] fusion d'images
[Termes IGN] image à haute résolution
[Termes IGN] image floue
[Termes IGN] image hyperspectrale
[Termes IGN] image multibande
[Termes IGN] image panchromatique
[Termes IGN] pansharpening (fusion d'images)
[Termes IGN] réseau neuronal profondRésumé : (Auteur) Traditional approaches mainly fuse a hyperspectral image (HSI) with a high-resolution multispectral image (MSI) to improve the spatial resolution of the HSI. However, such improvement in the spatial resolution of HSIs is still limited because the spatial resolution of MSIs remains low. To further improve the spatial resolution of HSIs, we propose HyperNet, a deep network for the fusion of HSI, MSI, and panchromatic image (PAN), which effectively injects the spatial details of an MSI and a PAN into an HSI while preserving the spectral information of the HSI. Thus, we design HyperNet on the basis of a uniform fusion strategy to solve the problem of complex fusion of three types of sources (i.e., HSI, MSI, and PAN). In particular, the spatial details of the MSI and the PAN are extracted by multiple specially designed multiscale-attention-enhance blocks in which multi-scale convolution is used to adaptively extract features from different reception fields, and two attention mechanisms are adopted to enhance the representation capability of features along the spectral and spatial dimensions, respectively. Through the capability of feature reuse and interaction in a specially designed dense-detail-insertion block, the previously extracted features are subsequently injected into the HSI according to the unidirectional feature propagation among the layers of dense connection. Finally, we construct an efficient loss function by integrating the multi-scale structural similarity index with the norm, which drives HyperNet to generate high-quality results with a good balance between spatial and spectral qualities. Extensive experiments on simulated and real data sets qualitatively and quantitatively demonstrate the superiority of HyperNet over other state-of-the-art methods. Numéro de notice : A2022-272 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2022.04.001 Date de publication en ligne : 07/04/2022 En ligne : https://doi.org/10.1016/j.isprsjprs.2022.04.001 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100461
in ISPRS Journal of photogrammetry and remote sensing > vol 188 (June 2022) . - pp 30 - 44[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2022061 SL Revue Centre de documentation Revues en salle Disponible 081-2022063 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2022062 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt 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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PermalinkLine-based deep learning method for tree branch detection from digital images / Rodrigo L. S. Silva in International journal of applied Earth observation and geoinformation, vol 110 (June 2022)
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PermalinkPhysical modelling of Nanda Devi National Park, a natural world heritage site, from GIS data / Sanat Agrawal in Cartographica, vol 57 n° 2 (Summer 2022)
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PermalinkRecent advances in forest insect pests and diseases monitoring using UAV-based data: A systematic review / André Duarte in Forests, vol 13 n° 6 (June 2022)
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