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Street-level traffic flow and context sensing analysis through semantic integration of multisource geospatial data / Yatao Zhang in Transactions in GIS, vol 26 n° 8 (December 2022)
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Titre : Street-level traffic flow and context sensing analysis through semantic integration of multisource geospatial data Type de document : Article/Communication Auteurs : Yatao Zhang, Auteur ; Martin Raubal, Auteur Année de publication : 2022 Article en page(s) : pp 3330 - 3348 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] allocation de Dirichlet latente
[Termes IGN] appariement sémantique
[Termes IGN] approche hiérarchique
[Termes IGN] données multisources
[Termes IGN] espace urbain
[Termes IGN] flux
[Termes IGN] milieu urbain
[Termes IGN] point d'intérêt
[Termes IGN] segmentation en régions
[Termes IGN] Singapour
[Termes IGN] trafic routier
[Termes IGN] utilisation du solRésumé : (auteur) Sensing urban spaces from multisource geospatial data is vital to understanding the transportation system in the urban context. However, the complexity of urban context and its indirect interaction with traffic flow deepen the difficulty of exploring their relationship. This study proposes a geo-semantic framework first to generate semantic representations of multi-hierarchical urban context and street-level traffic flow, and then investigate their mutual correlation and predictability using a novel semantic matching method. The results demonstrate that each street is associated with its multi-hierarchical spatial signatures of urban context and street-level temporal signatures of traffic flow. The correlation between urban context and traffic flow displays higher values after semantic matching than those in multi-hierarchies. Moreover, we found that utilizing traffic flow to predict urban context results in better accuracy than the reversed prediction. The results of signature analysis and relationship exploration can contribute to a deeper understanding of context-aware transportation research. Numéro de notice : A2022-916 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1111/tgis.13005 Date de publication en ligne : 27/11/2022 En ligne : https://doi.org/10.1111/tgis.13005 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102348
in Transactions in GIS > vol 26 n° 8 (December 2022) . - pp 3330 - 3348[article]Machine learning for spatial analyses in urban areas: a scoping review / Ylenia Casali in Sustainable Cities and Society, vol 85 (October 2022)
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Titre : Machine learning for spatial analyses in urban areas: a scoping review Type de document : Article/Communication Auteurs : Ylenia Casali, Auteur ; Nazli Yonca Aydin, Auteur ; Tina Comes, Auteur Année de publication : 2022 Article en page(s) : n° 104050 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] algorithme d'apprentissage
[Termes IGN] analyse spatio-temporelle
[Termes IGN] apprentissage automatique
[Termes IGN] distribution spatiale
[Termes IGN] espace urbain
[Termes IGN] littérature
[Termes IGN] source de données
[Termes IGN] urbanisme
[Termes IGN] ville durable
[Termes IGN] zone urbaineRésumé : (auteur) The challenges for sustainable cities to protect the environment, ensure economic growth, and maintain social justice have been widely recognized. Along with the digitization, availability of large datasets, Machine Learning (ML) and Artificial Intelligence (AI) are promising to revolutionize the way we analyze and plan urban areas, opening new opportunities for the sustainable city agenda. Especially urban spatial planning problems can benefit from ML approaches, leading to an increasing number of ML publications across different domains. What is missing is an overview of the most prominent domains in spatial urban ML along with a mapping of specific applied approaches. This paper aims to address this gap and guide researchers in the field of urban science and spatial data analysis to the most used methods and unexplored research gaps. We present a scoping review of ML studies that used geospatial data to analyze urban areas. Our review focuses on revealing the most prominent topics, data sources, ML methods and approaches to parameter selection. Furthermore, we determine the most prominent patterns and challenges in the use of ML. Through our analysis, we identify knowledge gaps in ML methods for spatial data science and data specifications to guide future research. Numéro de notice : A2022-765 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/URBANISME Nature : Article DOI : 10.1016/j.scs.2022.104050 Date de publication en ligne : 12/07/2022 En ligne : https://doi.org/10.1016/j.scs.2022.104050 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101786
in Sustainable Cities and Society > vol 85 (October 2022) . - n° 104050[article]A hierarchical model for semantic trajectories and event extraction in indoor and outdoor spaces / Hassan Noureddine (2022)
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Titre : A hierarchical model for semantic trajectories and event extraction in indoor and outdoor spaces Type de document : Thèse/HDR Auteurs : Hassan Noureddine, Auteur ; Christophe Claramunt, Directeur de thèse ; Cyril Ray, Directeur de thèse Editeur : Brest : Université de Bretagne Occidentale Année de publication : 2022 Importance : 113 p. Format : 21 x 30 cm Note générale : bibliographie
Thèse de Doctorat de l'Université de Bretagne Occidentale, spécialité GéomatiqueLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] approche hiérarchique
[Termes IGN] détection d'événement
[Termes IGN] données spatiotemporelles
[Termes IGN] espace intérieur
[Termes IGN] espace urbain
[Termes IGN] mobilité humaine
[Termes IGN] modèle sémantique de données
[Termes IGN] modélisation 3D
[Termes IGN] ville intelligenteIndex. décimale : THESE Thèses et HDR Résumé : (auteur) The interest in exploiting crowd-sourced information has recently emerged as it can bring many valuable benefits for many application domains. This is particularly the case for realtime mobile crowd-sourcing data often available in indoor and outdoor environments. Such data offers many opportunities for analysing human mobility, especially when associated with multidimensional contextual information. Considering the diversity of multi-environment spaces and where mobility occurs, raises several data modelling, management and processing research challenges.When associated with multiple contextual information, indoor and outdoor mobility analysis stresses the need for appropriate and flexible data abstractions at the modelling level to represent the spatial, temporal and semantic data that arise in a smart city environment. While recent approaches often considered this issue using the common stops and moves model, this does not completely cover the multi-dimensional contextual information that arises in real-time on humans navigating through indoor and outdoor spaces. It also increases the need for computing systems and data architectures to process spatio-temporal data in a timely manner when searching for complex mobility events of interest. Despite the ability to represent spatio-temporal events, such systems require well-defined and flexible data manipulation languages to support abstraction and composition mechanisms for analysing urban mobilities.This thesis aims to provide the necessary constructs for analysing mobile crowd-sensed information that arises in indoor and outdoor spaces. In order to better understand urban mobility data in batch and real-time, we consider a broad range of contextual information that can be associated with mobility data. We introduce an indoor and outdoor spatial data model represented as a multi-layered graph and constructed with crowd-sourced trajectory data. The novelty of the approach lies in the fact that it provides a homogeneous and flexible hierarchical indoor and outdoor spatial model that can be associated with crowd-sensed trajectory data on the fly. Our modelling approach defines generic and flexible semantic trajectories considering multiple collaborative data semantics at different granularities and where trajectory segmentation relies on evolving semantic values. This thesis develops a modelling framework for complex events applied to our indoor and outdoor semantic trajectory model based on a formallanguage that establishes the required operations for the composition of the events. We have implemented data pipelines to examine our approach’s efficiency. The whole approach is experimented and applied to participatory data from a real case study to show its suitability, scalability and performance. Note de contenu : 1- Introduction
2- Related work
3- Semantic trajectory data model For indoor and outdoor spaces
4- Composite event extraction from stream of semantic trajectories
5- ConclusionNuméro de notice : 24080 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Thèse française Note de thèse : Thèse de Doctorat : Géomatique : Brest : 2022 Organisme de stage : Institut de Recherche de l’Ecole Navale DOI : sans En ligne : https://theses.hal.science/tel-03888591 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102293 The use of Otsu algorithm and multi-temporal airborne LiDAR data to detect building changes in urban space / Renato César Dos santos in Applied geomatics, vol 13 n° 4 (December 2021)
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Titre : The use of Otsu algorithm and multi-temporal airborne LiDAR data to detect building changes in urban space Type de document : Article/Communication Auteurs : Renato César Dos santos, Auteur ; Mauricio Galo, Auteur ; André C. Carrilho, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 499 - 513 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] algorithme de Otsu
[Termes IGN] analyse de groupement
[Termes IGN] Brésil
[Termes IGN] détection de changement
[Termes IGN] détection du bâti
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] données multitemporelles
[Termes IGN] espace urbain
[Termes IGN] modèle numérique de surface
[Termes IGN] planéité
[Termes IGN] semis de points
[Termes IGN] seuillageRésumé : (auteur) Building change detection techniques are essential for several urban applications. In this context, multi-temporal airborne LiDAR data has been considered an effective alternative since it has some advantages over conventional photogrammetry. Despite several works in the literature, the automatic class definition with great accuracy and performance remains a challenge in change detection. The developed strategies usually explore training samples or empirical thresholds to discriminate the classes. To overcome this limitation, we proposed an automatic building change detection method based on Otsu algorithm and median planarity attribute computed from eigenvalues. The main contribution corresponds to the automatic and unsupervised identification of building changes. The experiments were conducted using airborne LiDAR data from two epochs: 2012 and 2014. From qualitative and quantitative analysis, the robustness of the proposed method in detecting building changes in urban areas was evaluated, presenting completeness and correctness around 99% and 76%, respectively. Numéro de notice : A2021-856 Affiliation des auteurs : non IGN Thématique : IMAGERIE/URBANISME Nature : Article DOI : 10.1007/s12518-021-00371-6 Date de publication en ligne : 24/04/2021 En ligne : https://doi.org/10.1007/s12518-021-00371-6 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99062
in Applied geomatics > vol 13 n° 4 (December 2021) . - pp 499 - 513[article]A spatial model of cognitive distance in cities / Ed Manley in International journal of geographical information science IJGIS, vol 35 n° 11 (November 2021)
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Titre : A spatial model of cognitive distance in cities Type de document : Article/Communication Auteurs : Ed Manley, Auteur ; Gabriele Filomena, Auteur ; Panos Mavros, Auteur Année de publication : 2021 Article en page(s) : pp 2316 - 2338 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] cognition
[Termes IGN] distance
[Termes IGN] espace euclidien
[Termes IGN] espace urbain
[Termes IGN] modélisation spatiale
[Termes IGN] morphologie urbaine
[Termes IGN] perception
[Termes IGN] positionnement statique
[Termes IGN] représentation mentale spatiale
[Termes IGN] système d'information urbainRésumé : (auteur) Spatial cognition is fundamental to the behaviour and activity of humans in urban space. Humans perceive their environments with systematic biases and errors, and act upon these perceptions, which in turn form urban patterns of activity. These perceptions are influenced by a multitude of factors, many of them relating to the static urban form. Yet much of geographic analysis ignores the influence of urban form, instead referring most commonly to the Euclidean arrangement of space. In this paper, we propose a novel spatial modelling framework for estimating cognitive distance in urban space. This framework is constructed from a wealth of research describing the effect of environmental factors on distance estimation, and produces a quantitative estimate of the effect based on standard GIS data. Unlike other cost measures, the cognitive distance estimate integrates systematically observed distortions and biases in spatial cognition. As a proof-of-concept, the framework is implemented for 26 cities worldwide using open data, producing a novel comparative measure of ‘cognitive accessibility’. The paper concludes with a discussion of the potential of this approach in analysing and modelling urban systems, and outlines areas for further research. Numéro de notice : A2021-761 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2021.1887488 Date de publication en ligne : 19/02/2021 En ligne : https://doi.org/10.1080/13658816.2021.1887488 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98790
in International journal of geographical information science IJGIS > vol 35 n° 11 (November 2021) . - pp 2316 - 2338[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 079-2021111 SL Revue Centre de documentation Revues en salle Disponible Structure-aware completion of photogrammetric meshes in urban road environment / Qing Zhu in ISPRS Journal of photogrammetry and remote sensing, vol 175 (May 2021)
PermalinkExploring the heterogeneity of human urban movements using geo-tagged tweets / Ding Ma in International journal of geographical information science IJGIS, vol 34 n° 12 (December 2020)
PermalinkComparing pedestrians’ gaze behavior in desktop and in real environments / Weihua Dong in Cartography and Geographic Information Science, Vol 47 n° 5 (September 2020)
PermalinkRecognition of building group patterns using graph convolutional network / Rong Zhao in Cartography and Geographic Information Science, Vol 47 n° 5 (September 2020)
PermalinkA name‐led approach to profile urban places based on geotagged Twitter data / Juntao Lai in Transactions in GIS, Vol 24 n° 4 (August 2020)
PermalinkFine-scale dasymetric population mapping with mobile phone and building use data based on grid Voronoi method / Zhenzhong Peng in ISPRS International journal of geo-information, vol 9 n° 6 (June 2020)
PermalinkAn agent-based model of public space use / Kostas Cheliotis in Computers, Environment and Urban Systems, Vol 81 (May 2020)
PermalinkA framework for extracting urban functional regions based on multiprototype word embeddings using points-of-interest data / Sheng Hu in Computers, Environment and Urban Systems, vol 80 (March 2020)
PermalinkPermalinkCartographic delimitation of the city centre using mental sketches / Kamil Nieścioruk in Cartographic journal (the), Vol 56 n° 4 (November 2019)
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