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An improved optimization model for crowd evacuation considering individual exit choice preference / Fei Gao in Transactions in GIS, vol 26 n° 7 (November 2022)
[article]
Titre : An improved optimization model for crowd evacuation considering individual exit choice preference Type de document : Article/Communication Auteurs : Fei Gao, Auteur ; Zhiqiang Du, Auteur ; Martin Werner, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 2850 - 2873 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] comportement
[Termes IGN] événement
[Termes IGN] gestion de crise
[Termes IGN] optimisation (mathématiques)
[Termes IGN] optimisation par essaim de particules
[Termes IGN] planification
[Termes IGN] secours d'urgenceRésumé : (auteur) Guidance-assisted crowd evacuation is a process of combining individual exit choice behavior with managers'exit assignment control. The knowledge of individual exit choice preference is of great significance for optimizing global exit assignment planning. This study proposes an improved optimization model for crowd evacuation by integrating the individual-level exit choice preference analysis with system-level exit assignment optimization to represent more realistic crowd evacuation decisions. First, the impact factors of individual exit choice behavior are considered in a mixed logit model to predict the probability of each individual choosing each exit in specific situations. Second, a preference-based exit filtering strategy is designed to analyze the sensible alternative exits for individuals or groups in multi-scale evacuation cells. Finally, to pursue optimal exit assignment planning, a multi-objective particle swarm optimization algorithm and an improved social force model are adopted to simulate the process of crowd evacuation and evaluate the performance of the specific exit assignment plans. The case study of an outdoor multiple-exit scenario in Xi'an, China, indicates that the proposed model can help managers to understand the heterogeneity of individual evacuation behaviors. Furthermore, it will support more reliable and realistic evacuation decisions in real-life situations than conventional plans that typically implement the top-n strategy. Numéro de notice : A2022-833 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1111/tgis.12984 Date de publication en ligne : 04/09/2022 En ligne : https://doi.org/10.1111/tgis.12984 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102216
in Transactions in GIS > vol 26 n° 7 (November 2022) . - pp 2850 - 2873[article]An unsupervised framework for extracting multilane roads from OpenStreetMap / Kunkun Wu in International journal of geographical information science IJGIS, vol 36 n° 11 (November 2022)
[article]
Titre : An unsupervised framework for extracting multilane roads from OpenStreetMap Type de document : Article/Communication Auteurs : Kunkun Wu, Auteur ; Zhong Xie, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 2322 - 2344 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] analyse de groupement
[Termes IGN] apprentissage non-dirigé
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] extraction du réseau routier
[Termes IGN] OpenStreetMap
[Termes IGN] polygone
[Termes IGN] regroupement de pics de densité
[Termes IGN] route
[Termes IGN] segment de droite
[Vedettes matières IGN] GénéralisationRésumé : (auteur) Multilane roads are a set of approximately parallel line segments representing the same road in large-scale vector maps. They must be extracted first in cartographic generalization. There are numerous multilane roads in the easily accessible OpenStreetMap (OSM) dataset. For this dataset, polygon-based methods have achieved state-of-the-art performance. However, traditional polygon-based methods usually rely on manually labeled data, which means they are time-consuming and labor-intensive. To address this problem, an unsupervised framework for extracting multilane roads is proposed in this study. Road segments were first grouped to form the road polygons. A set of shape descriptors was formulated to reduce the dimensions of individual road polygons into conceptual points. Next, dimensional shape descriptors were standardized using logarithmic standardization. The density peaks clustering (DPC) algorithm was employed to classify these points. Then, cluster tags were identified manually to recognize which clusters represent multilane polygons. Finally, post-processing learning from the concept of assimilation is proposed to fill holes and remove islands. Experiments were conducted to extract multilane roads with datasets from three cities: Wuhan, Beijing and Munich. The experimental results show that the proposed framework effectively extracted multilane roads without any labels with accuracy levels comparable to those of supervised methods. Numéro de notice : A2022-797 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2022.2107208 Date de publication en ligne : 05/08/2022 En ligne : https://doi.org/10.1080/13658816.2022.2107208 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101956
in International journal of geographical information science IJGIS > vol 36 n° 11 (November 2022) . - pp 2322 - 2344[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 079-2022111 SL Revue Centre de documentation Revues en salle Disponible Associating land registry and cadastre transactions with LADM-based external archive data model: a case study of Turkey / Zeynel Abidin Polat in Survey review, vol 54 n° 387 (November 2022)
[article]
Titre : Associating land registry and cadastre transactions with LADM-based external archive data model: a case study of Turkey Type de document : Article/Communication Auteurs : Zeynel Abidin Polat, Auteur ; Mehmet Alkan, Auteur Année de publication : 2022 Article en page(s) : pp 519 - 533 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Cadastre étranger
[Termes IGN] application web
[Termes IGN] base de données foncières
[Termes IGN] données cadastrales
[Termes IGN] infrastructure nationale des données localisées
[Termes IGN] système d'information territorial
[Termes IGN] TurquieRésumé : (auteur) In Turkey, during the realisation of the land registry and cadastre transactions, manually checking the official documents exceeding 20 million annually causes some problems in document management and security. This study focuses on managing the official documents as online requested by the land registry and cadastre directorates from the other institutions during these transactions. Therefore, a general external data model based on the LADM has been proposed to manage requested documents during transactions. If web-based applications support the proposed model, access to documents will be faster and easier, and security risk will be reduced. Numéro de notice : A2022-830 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/00396265.2021.1970919 Date de publication en ligne : 01/09/2021 En ligne : https://doi.org/10.1080/00396265.2021.1970919 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102015
in Survey review > vol 54 n° 387 (November 2022) . - pp 519 - 533[article]Geographic information system data considerations in the context of the enhanced bathtub model for coastal inundation / Lauren Lyn Williams in Transactions in GIS, vol 26 n° 7 (November 2022)
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Titre : Geographic information system data considerations in the context of the enhanced bathtub model for coastal inundation Type de document : Article/Communication Auteurs : Lauren Lyn Williams, Auteur ; Melanie Lück-Vogel, Auteur Année de publication : 2022 Article en page(s) : pp 3074 - 3089 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG
[Termes IGN] Afrique du sud (état)
[Termes IGN] ArcGIS
[Termes IGN] données lidar
[Termes IGN] milieu urbain
[Termes IGN] modèle numérique de surface
[Termes IGN] semis de points
[Termes IGN] submersion marine
[Termes IGN] système d'information géographiqueRésumé : (auteur) concerning digital surface models (DSMs) to determine: (a) the highest appropriate resolution achievable from available LiDAR data and consider variations between derived sub-meter DSMs; (b) optimal DSM horizontal resolution for coastal inundation modeling based on “out-the-box” solutions; and (c) mechanisms to address the challenge presented by DSMs regarding overhanging structures for a study site in False Bay, South Africa. Results showed that while sub-meter DSMs are achievable, low point cloud densities may result in the misrepresentation of structures, which affects the inundation extents. High horizontal resolution DSMs are required for inundation modeling in an urban setting to account for narrow thoroughfares. Challenges posed by first return LiDAR depicting bridges as solid structures could be circumvented by modifying the input water source for the eBTM processing. Numéro de notice : A2022-888 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE Nature : Article DOI : 10.1111/tgis.12995 Date de publication en ligne : 18/10/2022 En ligne : https://doi.org/10.1111/tgis.12995 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102232
in Transactions in GIS > vol 26 n° 7 (November 2022) . - pp 3074 - 3089[article]Geographically convolutional neural network weighted regression: a method for modeling spatially non-stationary relationships based on a global spatial proximity grid / Zhen Dai in International journal of geographical information science IJGIS, vol 36 n° 11 (November 2022)
[article]
Titre : Geographically convolutional neural network weighted regression: a method for modeling spatially non-stationary relationships based on a global spatial proximity grid Type de document : Article/Communication Auteurs : Zhen Dai, Auteur ; Sensen Wu, Auteur ; Yuanyuan Wang, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 2248 - 2269 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] distribution spatiale
[Termes IGN] modèle de régression
[Termes IGN] régression géographiquement pondérée
[Termes IGN] régression linéaire
[Termes IGN] réseau neuronal convolutifRésumé : (auteur) Geographically weighted regression (GWR) is a classical method of modeling spatially non-stationary relationships. The geographically neural network weighted regression (GNNWR) model solves the problem of the inaccurate construction of spatial weight kernels using a spatially weighted neural network. However, when the spatial distribution of observations is uneven, the spatial proximity expression in the input of GWR and GNNWR models does not fully represent the impact of the whole research space on the estimating point. Therefore, we established a global spatial proximity grid (GSPG) to express the spatial proximity of each estimating point and proposed a spatially weighted convolutional neural network (SWCNN) to extract the relationship between the GSPG and spatial weights. Finally, we proposed a geographically convolutional neural network weighted regression (GCNNWR) model combining SWCNN and ordinary linear regression (OLR) model to estimate spatial non-stationarity. We used two case studies of simulated data and real environment data to demonstrate the advancements of the GCNNWR model. The GCNNWR model achieved higher estimation accuracy and greater predictive power than the OLR, GWR, multi-scale GWR (MGWR), and GNNWR models. Moreover, the GCNNWR model maintained its better stability and accuracy in estimating spatially non-stationary relationships when the distribution of observations was uneven. Numéro de notice : A2022-773 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2022.2100892 Date de publication en ligne : 27/09/2022 En ligne : https://doi.org/10.1080/13658816.2022.2100892 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101954
in International journal of geographical information science IJGIS > vol 36 n° 11 (November 2022) . - pp 2248 - 2269[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 079-2022111 SL Revue Centre de documentation Revues en salle Disponible Graph-based leaf–wood separation method for individual trees using terrestrial lidar point clouds / Zhilin Tian in IEEE Transactions on geoscience and remote sensing, vol 60 n° 11 (November 2022)PermalinkGraph neural networks with constraints of environmental consistency for landslide susceptibility evaluation / Haowei Zeng in International journal of geographical information science IJGIS, vol 36 n° 11 (November 2022)PermalinkImproving accuracy of local geoid model using machine learning approaches and residuals of GPS/levelling geoid height / Mosbeh R. Kaloop in Survey review, vol 54 n° 387 (November 2022)PermalinkImproving deep learning on point cloud by maximizing mutual information across layers / Di Wang in Pattern recognition, vol 131 (November 2022)PermalinkA joint deep learning network of point clouds and multiple views for roadside object classification from lidar point clouds / Lina Fang in ISPRS Journal of photogrammetry and remote sensing, vol 193 (November 2022)PermalinkMachine learning and landslide studies: recent advances and applications / Faraz S. Tehrani in Natural Hazards, vol 114 n° 2 (November 2022)PermalinkA machine learning approach for detecting rescue requests from social media / Zheye Wang in ISPRS International journal of geo-information, vol 11 n° 11 (November 2022)PermalinkMachine learning models applied to a GNSS sensor network for automated bridge anomaly detection / Nicolas Manzini in Journal of structural engineering, Vol 148 n° 11 (November 2022)PermalinkMapping forest in the Swiss Alps treeline ecotone with explainable deep learning / Thiên-Anh Nguyen in Remote sensing of environment, vol 281 (November 2022)PermalinkMeasuring visual walkability perception using panoramic street view images, virtual reality, and deep learning / Yunqin Li in Sustainable Cities and Society, vol 86 (November 2022)Permalink