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Termes IGN > aménagement > urbanisme
urbanisme
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Aménagement urbain, Développement urbain, Habitat (urbanisme), Planification urbaine, Ville modèle. Synonyme(s)aménagement urbainVoir aussi |
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Characterizing the spatial and temporal variation of the land surface temperature hotspots in Wuhan from a local scale / Chen Yang in Geo-spatial Information Science, vol 23 n° 4 (December 2020)
[article]
Titre : Characterizing the spatial and temporal variation of the land surface temperature hotspots in Wuhan from a local scale Type de document : Article/Communication Auteurs : Chen Yang, Auteur ; Qingming Zhan, Auteur ; Sihang Gao, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 327 - 340 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse spatio-temporelle
[Termes IGN] climat urbain
[Termes IGN] géomorphologie locale
[Termes IGN] ilot thermique urbain
[Termes IGN] image Landsat-OLI
[Termes IGN] image Terra-MODIS
[Termes IGN] image thermique
[Termes IGN] morphologie urbaine
[Termes IGN] processus gaussien
[Termes IGN] regroupement de données
[Termes IGN] série temporelle
[Termes IGN] température au sol
[Termes IGN] Wuhan (Chine)
[Termes IGN] zonage (urbanisme)Résumé : (auteur) Land Surface Temperature (LST) derived from space-borne Thermal-infrared (TIR) sensors is a key parameter of urban climate studies. Current studies are inefficient to capture the spatial and temporal variations of LST for only one snapshot adopted at one time. Focusing on the characterization of the spatial and temporal of LST variations at local scales, the latent patterns, and morphological characteristics are extracted in this study. Technically, sixteen MODerate-resolution Imaging Spectroradiometer (MODIS) eight-day synthesized LST products (MYD11A2) in 2002, 2007, 2012, and 2017 are employed. First, the non-parametric Multi-Task Gaussian Process Model (MTGP) is used to extract the smooth and continuous Latent LST (LLST) patterns using one LST subset and its temporally adjacent images. Second, the Multi-Scale Shape Index (MSSI) is then applied to quantify the morphological characteristics at the optimal scale. Then, the LLST patterns and MSSI maps are clustered into multiple spatial categories. The specific clusters with the highest LLST and MSSI values are considered as local LLST hotspots. The Hotspots Weighted Mean Center (HSWMC) and standard deviation ellipse are adopted to further investigate the spatiotemporal change of hotspots orientation, direction, and trajectories. Results revealed that Impervious Surfaces (IS) composition is the most significant external forcing of local LST anomalies. The configuration factors (e.g., shape index, aggregation index) also have a noticeable local warming effect. This study represents a latent pattern and morphology-based framework for LST hotspots spatial and temporal variations characterization, catering to the zoning and grading strategies in urban planning. Numéro de notice : A2020-788 Affiliation des auteurs : non IGN Thématique : IMAGERIE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10095020.2020.1834882 Date de publication en ligne : 06/11/2020 En ligne : https://doi.org/10.1080/10095020.2020.1834882 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96550
in Geo-spatial Information Science > vol 23 n° 4 (December 2020) . - pp 327 - 340[article]How urban places are visited by social groups? Evidence from matrix factorization on mobile phone data / Chaogui Kang in Transactions in GIS, Vol 24 n° 6 (December 2020)
[article]
Titre : How urban places are visited by social groups? Evidence from matrix factorization on mobile phone data Type de document : Article/Communication Auteurs : Chaogui Kang, Auteur ; Li Shi, Auteur ; Fahui Wang, Auteur ; Yu Liu, Auteur Année de publication : 2020 Article en page(s) : pp 1504 - 1525 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] Chine
[Termes IGN] contenu généré par les utilisateurs
[Termes IGN] données spatiotemporelles
[Termes IGN] ethnographie
[Termes IGN] factorisation de matrice non-négative
[Termes IGN] matrice de co-occurrence
[Termes IGN] production participative
[Termes IGN] réseau social
[Termes IGN] site urbain
[Termes IGN] téléphonie mobile
[Termes IGN] urbanismeRésumé : (Auteur) This research attempts to build a unified framework for distinguishing the spatiotemporal visit patterns of urban places by different social groups using mobile phone data in Harbin, China. Social groups are detected by their social ties in the ego‐to‐ego mobile phone call network and are embedded in physical space according to their home locations. Popular urban places are detected from user‐generated content as the basic spatial analysis unit. Coupling subscribers’ footprints and urban places in physical space, the spatiotemporal visit patterns of urban places by distinct social groups are uncovered and interpreted by non‐negative matrix factorization. The proposed framework enables us to answer several critical questions from three perspectives: (1) How to model popular urban places in terms of vague boundary, land use, and semantic features based on crowdsourcing data?; (2) How to evaluate interaction between individuals for inspecting the relationship between spatial proximity and social ties based on spatiotemporal co‐occurrence?; and (3) How to distinguish urban place visit preferences for social groups associated with different socio‐demographic characteristics? Our research could assist urban planners and municipal managers to identify critical urban places frequented by different population groups according to their roles and social/cultural characteristics for improvement of urban facility allocation. Numéro de notice : A2020-767 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12654 Date de publication en ligne : 30/06/2020 En ligne : https://doi.org/10.1111/tgis.12654 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96658
in Transactions in GIS > Vol 24 n° 6 (December 2020) . - pp 1504 - 1525[article]Innovative approaches, tools and visualization techniques for analysing land use structures and dynamics of cities and regions (Editorial) / Robert Hecht in Journal of Geovisualization and Spatial Analysis, vol 4 n° 2 (December 2020)
[article]
Titre : Innovative approaches, tools and visualization techniques for analysing land use structures and dynamics of cities and regions (Editorial) Type de document : Article/Communication Auteurs : Robert Hecht, Auteur ; Martin Behnisch, Auteur Année de publication : 2020 Article en page(s) : n° 19 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] analyse géovisuelle
[Termes IGN] analyse spatiale
[Termes IGN] changement d'occupation du sol
[Termes IGN] croissance urbaine
[Termes IGN] utilisation du sol
[Termes IGN] visualisation cartographique
[Vedettes matières IGN] GéovisualisationRésumé : (auteur) Geospatial analysis and visualisation can be used to gain knowledge about land use structure and its changes on different spatial scales, which in turn is key to unlock the potential for sustainable land use development. This editorial provides a frame to a set of papers of the topical collection “Innovative approaches, tools and visualization techniques for analyzing land use structures and dynamics of cities and regions”, which was initiated in conjunction with the 2017 International Land Use Symposium taken place in Dresden, Germany. It first introduces current, urging land use, development and management challenges. Further on, the editorial presents the individual contributions and reflects their affiliation to the themes “Mapping and Monitoring Approaches” and “Planning, Decision Support and Participation”. Although the objectives, methods and underlying data used in the papers of this topical collection greatly vary, as pieces of a puzzle they contribute to a better analysis and understanding of current and future land use structures and dynamics of cities and regions. Numéro de notice : A2020-797 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s41651-020-00060-9 Date de publication en ligne : 10/08/2020 En ligne : https://doi.org/10.1007/s41651-020-00060-9 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96614
in Journal of Geovisualization and Spatial Analysis > vol 4 n° 2 (December 2020) . - n° 19[article]Learning from urban form to predict building heights / Nikola Milojevic-Dupont in Plos one, vol 15 n° 12 (December 2020)
[article]
Titre : Learning from urban form to predict building heights Type de document : Article/Communication Auteurs : Nikola Milojevic-Dupont, Auteur ; Nicolai Hans, Auteur ; Lynn H. Kaack, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : n° 0242010 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] Allemagne
[Termes IGN] apprentissage automatique
[Termes IGN] base de connaissances
[Termes IGN] données localisées des bénévoles
[Termes IGN] France (administrative)
[Termes IGN] hauteur du bâti
[Termes IGN] Italie
[Termes IGN] morphologie urbaine
[Termes IGN] OpenStreetMap
[Termes IGN] Pays-Bas
[Termes IGN] villeRésumé : (auteur) Understanding cities as complex systems, sustainable urban planning depends on reliable high-resolution data, for example of the building stock to upscale region-wide retrofit policies. For some cities and regions, these data exist in detailed 3D models based on real-world measurements. However, they are still expensive to build and maintain, a significant challenge, especially for small and medium-sized cities that are home to the majority of the European population. New methods are needed to estimate relevant building stock characteristics reliably and cost-effectively. Here, we present a machine learning based method for predicting building heights, which is based only on open-access geospatial data on urban form, such as building footprints and street networks. The method allows to predict building heights for regions where no dedicated 3D models exist currently. We train our model using building data from four European countries (France, Italy, the Netherlands, and Germany) and find that the morphology of the urban fabric surrounding a given building is highly predictive of the height of the building. A test on the German state of Brandenburg shows that our model predicts building heights with an average error well below the typical floor height (about 2.5 m), without having access to training data from Germany. Furthermore, we show that even a small amount of local height data obtained by citizens substantially improves the prediction accuracy. Our results illustrate the possibility of predicting missing data on urban infrastructure; they also underline the value of open government data and volunteered geographic information for scientific applications, such as contextual but scalable strategies to mitigate climate change. Numéro de notice : A2020-830 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Article DOI : 10.1371/journal.pone.0242010 Date de publication en ligne : 09/12/2020 En ligne : https://doi.org/10.1371/journal.pone.0242010 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97658
in Plos one > vol 15 n° 12 (December 2020) . - n° 0242010[article]Multistrategy ensemble regression for mapping of built-up density and height with Sentinel-2 data / Christian Geiss in ISPRS Journal of photogrammetry and remote sensing, vol 170 (December 2020)
[article]
Titre : Multistrategy ensemble regression for mapping of built-up density and height with Sentinel-2 data Type de document : Article/Communication Auteurs : Christian Geiss, Auteur ; Henrik Schrade, Auteur ; Patrick Aravena Pelizari, Auteur ; Hannes Taubenböck, Auteur Année de publication : 2020 Article en page(s) : pp 57-71 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] Allemagne
[Termes IGN] apprentissage automatique
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] hauteur du bâti
[Termes IGN] image multibande
[Termes IGN] image Sentinel-MSI
[Termes IGN] image TanDEM-X
[Termes IGN] modèle de régression
[Termes IGN] morphologie urbaine
[Termes IGN] pondération
[Termes IGN] processus gaussien
[Termes IGN] zone urbaine denseRésumé : (Auteur) In this paper, we establish a workflow for estimation of built-up density and height based on multispectral Sentinel-2 data. To do so, we render the estimation of built-up density and height as a supervised learning problem. Given the rational level of measurement of those two target variables, the regression estimation problem is regarded as finding the mapping between an incoming vector, i.e., ubiquitously available features computed from Sentinel-2 data, and an observable output (i.e., training set), which is derived over spatially limited areas in an automated manner. As such, training sets are automatically generated from a joint exploitation of TanDEM-X mission elevation data and Sentinel-2 imagery, and, as an alternative, from cadastral sources. The training sets are used to regress the target variables for spatial processing units which correspond to urban neighborhood scales. From a methodological point of view, we introduce a novel ensemble regression approach, i.e., multistrategy ensemble regression (MSER), based on advanced machine learning-based regression algorithms including Random Forest Regression, Support Vector Regression, Gaussian Process Regression, and Neural Network Regression. To establish a robust ensemble, those algorithms are learned with a modified version of the AdaBoost.RT algorithm. However, to reliably ensure diversity between single boosted regressors, we include a random feature subspace method in the procedure. In contrast to existing approaches, we selectively prune non-favorable regressors trained during the boosting procedure and calculate the final prediction by a weighted mean function on the residual models to ensure enhanced accuracy properties of predictions. Finally, outputs are concatenated into a single prediction with a decision fusion strategy. Experimental results are obtained from four test areas which cover the settlement areas of the four largest German cites, i.e., Berlin, Hamburg, Munich, and Cologne. The results unambiguously underline the beneficial properties of the MSER approach, since all best predictions were obtained with a boosted regressor in conjunction with a decision fusion strategy in a comparative setup. The mean absolute errors of corresponding models vary between 3 and 16% and 1–5.4 m with respect to built-up density and height, respectively, depending on the validation strategy, size of the spatial processing units, and test area. Also in a domain adaptation setup (i.e., when learning a model over a source domain and applying it over a geographically different target domain) numerous predictions show comparable accuracy levels as predictions obtained within a source domain. This further underlines the viability to transfer a model and, thus, enable a substitution of the training data in the target domains. Numéro de notice : A2020-704 Affiliation des auteurs : non IGN Thématique : IMAGERIE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.10.004 Date de publication en ligne : 22/10/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.10.004 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96231
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