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Spatial multi-criteria evaluation in 3D context: suitability analysis of urban vertical development / Kendra Munn in Cartography and Geographic Information Science, vol 48 n° 2 (March 2021)
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Titre : Spatial multi-criteria evaluation in 3D context: suitability analysis of urban vertical development Type de document : Article/Communication Auteurs : Kendra Munn, Auteur ; Suzana Dragićević, Auteur Année de publication : 2021 Article en page(s) : pp 105 - 123 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes descripteurs IGN] analyse multicritère
[Termes descripteurs IGN] combinaison linéaire ponderée
[Termes descripteurs IGN] compréhension de l'image
[Termes descripteurs IGN] croissance urbaine
[Termes descripteurs IGN] densification
[Termes descripteurs IGN] hauteur du bâti
[Termes descripteurs IGN] logement
[Termes descripteurs IGN] modèle 3D de l'espace urbain
[Termes descripteurs IGN] planification urbaine
[Termes descripteurs IGN] urbanisme
[Termes descripteurs IGN] Vancouver (Colombie britannique)Résumé : (Auteur) Urban densification is often seen as a process that aims to limit the negative environmental impacts of urban sprawl in rapidly growing cities by prioritizing planning policies stimulating vertical growth (or high-rise development) over expansion along the urban fringe. Densification of major Canadian urban areas has led to the proliferation of high-rises with an increasing proportion of residents occupying these buildings rather than traditional individual housing. Thus, there is a need for analytical methods that can evaluate the suitability of different residential units in vertical urban developments based on unique criteria for different stakeholders such as prospective residents, developers, or municipal planners. Multi-criteria evaluation (MCE) analysis with weighted linear combination (WLC) is frequently implemented in geographic information systems (GIS) to identify the appropriate solution(s) for a decision problem. However, there are currently no available MCE methods for spatial analysis that can provide evaluation in a three-dimensional (3D) GIS environment, such as for urban vertical development. Therefore, the main objective of this study is to propose a 3D WLC-MCE suitability analysis method for suitability of high-rise residential units in a dense urban area. Five preference scenarios were developed and applied to data from City of Vancouver, Canada. The results indicate that south-facing units and units on higher floors generally exhibit higher levels of suitability as they are less affected by the noise and pollution of the urban road network and receive more sunlight and ocean views. The proposed 3D MCE approach can be used for urban planning and property tax assessment. Numéro de notice : A2021-096 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/15230406.2020.1845981 date de publication en ligne : 03/12/2020 En ligne : https://doi.org/10.1080/15230406.2020.1845981 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97020
in Cartography and Geographic Information Science > vol 48 n° 2 (March 2021) . - pp 105 - 123[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)
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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 descripteurs IGN] Allemagne
[Termes descripteurs IGN] apprentissage automatique
[Termes descripteurs IGN] classification par forêts aléatoires
[Termes descripteurs IGN] classification par séparateurs à vaste marge
[Termes descripteurs IGN] hauteur du bâti
[Termes descripteurs IGN] image multibande
[Termes descripteurs IGN] image Sentinel-MSI
[Termes descripteurs IGN] image TanDEM-X
[Termes descripteurs IGN] modèle de régression
[Termes descripteurs IGN] morphologie urbaine
[Termes descripteurs IGN] pondération
[Termes descripteurs IGN] processus gaussien
[Termes descripteurs 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
in ISPRS Journal of photogrammetry and remote sensing > vol 170 (December 2020) . - pp 57-71[article]A method for urban population density prediction at 30m resolution / Krishnachandran Balakrishnan in Cartography and Geographic Information Science, vol 47 n° 3 (May 2020)
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Titre : A method for urban population density prediction at 30m resolution Type de document : Article/Communication Auteurs : Krishnachandran Balakrishnan, Auteur Année de publication : 2020 Article en page(s) : pp 193 - 213 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes descripteurs IGN] densité de population
[Termes descripteurs IGN] gestion urbaine
[Termes descripteurs IGN] hauteur du bâti
[Termes descripteurs IGN] image Cartosat-1
[Termes descripteurs IGN] Inde
[Termes descripteurs IGN] logiciel de traitement d'image
[Termes descripteurs IGN] modèle de simulation
[Termes descripteurs IGN] modélisation du bâti
[Termes descripteurs IGN] système d'information géographique
[Termes descripteurs IGN] véhicule automobileRésumé : (auteur) This paper proposes a new method for urban population density prediction at 30 m resolution. Using data for Bangalore, the paper demonstrates that population within each 30 m residential built-up cell can be modeled as a function of cell-level data on street density and building heights and ward-level data on car ownership. Building-height data were generated from Cartosat-1 stereo imagery using an open-source satellite stereo image processing software. Using this building-height data in conjunction with the other datasets, the paper demonstrates that a 30 m resolution population density surface can be generated such that, when summed to the ward level, the median absolute percentage error between predicted population and known census population at the ward level is 8.29%. The paper also shows that the relationship between population density, street density, building height, and ward level car ownership is spatially non-stationary. A fine-grained understanding of urban population densities, as enabled by the proposed method, can be beneficial to research, policy, and practice related to cities. Numéro de notice : A2020-168 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/15230406.2019.1687014 date de publication en ligne : 18/12/2019 En ligne : https://doi.org/10.1080/15230406.2019.1687014 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94839
in Cartography and Geographic Information Science > vol 47 n° 3 (May 2020) . - pp 193 - 213[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 032-2020031 SL Revue Centre de documentation Revues en salle Disponible Integration of LiDAR and multispectral images for rapid exposure and earthquake vulnerability estimation. Application in Lorca, Spain / Yolanda Torres in International journal of applied Earth observation and geoinformation, vol 81 (September 2019)
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Titre : Integration of LiDAR and multispectral images for rapid exposure and earthquake vulnerability estimation. Application in Lorca, Spain Type de document : Article/Communication Auteurs : Yolanda Torres, Auteur ; José Juan Arranz, Auteur ; Jorge M. Gaspar-Escribano, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 161-175 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes descripteurs IGN] analyse d'image orientée objet
[Termes descripteurs IGN] apprentissage automatique
[Termes descripteurs IGN] données lidar
[Termes descripteurs IGN] empreinte
[Termes descripteurs IGN] Espagne
[Termes descripteurs IGN] extraction de traits caractéristiques
[Termes descripteurs IGN] hauteur du bâti
[Termes descripteurs IGN] image multibande
[Termes descripteurs IGN] image satellite
[Termes descripteurs IGN] orthophotographie
[Termes descripteurs IGN] risque urbain
[Termes descripteurs IGN] segmentation d'image
[Termes descripteurs IGN] séisme
[Termes descripteurs IGN] stratification de données
[Termes descripteurs IGN] vulnérabilité
[Termes descripteurs IGN] zone à risqueRésumé : (auteur) We present a procedure for assessing the urban exposure and seismic vulnerability that integrates LiDAR data with aerial images from the Spanish National Plan of Aerial Orthophotography (PNOA). It comprises three phases: first, we segment the satellite image to divide the study area into different urban patterns. Second, we extract building footprints and attributes that represent the type of building of each urban pattern. Finally, we assign the seismic vulnerability to each building using different machine-learning techniques: Decision trees, SVM, logistic regression and Bayesian networks. We apply the procedure to 826 buildings in the city of Lorca (SE Spain), where we count on a vulnerability database that we use as ground truth for the validation of results. The outcomes show that the machine learning techniques have similar performance, yielding vulnerability classification results with an accuracy of 77%–80% (F1-Score). The procedure is scalable and can be replicated in different areas. This is particularly relevant in Spain, where more than seven hundred towns have to develop seismic risk studies in the years to come, according to the General Direction of Civil Protection and Emergencies. It is especially interesting as a complement to conventional data gathering approaches for disaster risk applications in cities where field surveys need to be restricted to certain areas, dates or budget. Numéro de notice : A2019-471 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.jag.2019.05.015 date de publication en ligne : 25/05/2019 En ligne : https://doi.org/10.1016/j.jag.2019.05.015 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93617
in International journal of applied Earth observation and geoinformation > vol 81 (September 2019) . - pp 161-175[article]Satellite images analysis for shadow detection and building height estimation / Gregoris Liasis in ISPRS Journal of photogrammetry and remote sensing, vol 119 (September 2016)
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Titre : Satellite images analysis for shadow detection and building height estimation Type de document : Article/Communication Auteurs : Gregoris Liasis, Auteur ; Stavros Stavrou, Auteur Année de publication : 2016 Article en page(s) : pp 437 - 450 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes descripteurs IGN] contour
[Termes descripteurs IGN] délimitation
[Termes descripteurs IGN] détection d'ombre
[Termes descripteurs IGN] filtre spectral
[Termes descripteurs IGN] hauteur du bâti
[Termes descripteurs IGN] image satellite
[Termes descripteurs IGN] intensité lumineuse
[Termes descripteurs IGN] ombre
[Termes descripteurs IGN] scène urbaine
[Termes descripteurs IGN] valeur radiométriqueRésumé : (Auteur) Satellite images can provide valuable information about the presented urban landscape scenes to remote sensing and telecommunication applications. Obtaining information from satellite images is difficult since all the objects and their surroundings are presented with feature complexity. The shadows cast by buildings in urban scenes can be processed and used for estimating building heights. Thus, a robust and accurate building shadow detection process is important. Region-based active contour models can be used for satellite image segmentation. However, spectral heterogeneity that usually exists in satellite images and the feature similarity representing the shadow and several non-shadow regions makes building shadow detection challenging. In this work, a new automated method for delineating building shadows is proposed. Initially, spectral and spatial features of the satellite image are utilized for designing a custom filter to enhance shadows and reduce intensity heterogeneity. An effective iterative procedure using intensity differences is developed for tuning and subsequently selecting the most appropriate filter settings, able to highlight the building shadows. The response of the filter is then used for automatically estimating the radiometric property of the shadows. The customized filter and the radiometric feature are utilized to form an optimized active contour model where the contours are biased to delineate shadow regions. Post-processing morphological operations are also developed and applied for removing misleading artefacts. Finally, building heights are approximated using shadow length and the predefined or estimated solar elevation angle. Qualitative and quantitative measures are used for evaluating the performance of the proposed method for both shadow detection and building height estimation. Numéro de notice : A2016-792 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern En ligne : http://dx.doi.org/10.1016/j.isprsjprs.2016.07.006 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82509
in ISPRS Journal of photogrammetry and remote sensing > vol 119 (September 2016) . - pp 437 - 450[article]Measuring urban volume: geospatial technique and application / Ronald C. Estoque in Tsukuba geoenvironmental sciences, vol 11 ([01/12/2015])
Permalink3D model construction in an urban environment from sparse LiDAR points and aerial photos : a statistical approach / Xuebin Wei in Geomatica [en ligne], vol 69 n° 3 (september 2015)
PermalinkUtilisation de la 3D à Metz-métropole (2) / Thomas Dalstein in Géomatique expert, n° 100 (01/09/2014)
PermalinkBuilding-damage detection using pre- and post-seismic high-resolution satellite stereo imagery: A case study of the May 2008 Wenchuan earthquake / X. Tong in ISPRS Journal of photogrammetry and remote sensing, vol 68 (March 2012)
PermalinkGeolocation and stereo height estimation using TerraSAR-X spotlight image data / K. Eldhuset in IEEE Transactions on geoscience and remote sensing, vol 49 n° 10 Tome 1 (October 2011)
PermalinkBuilding roof modeling from airborne laser scanning data based on level set approach / K. Kim in ISPRS Journal of photogrammetry and remote sensing, vol 66 n° 4 (July - August 2011)
PermalinkAggregation of LoD 1 building models as an optimization problem / R. Guercke in ISPRS Journal of photogrammetry and remote sensing, vol 66 n° 2 (March - April 2011)
PermalinkAutomatic reasoning for geometric constraints in 3D city models with uncertain observations / S. Loch-Dehbi in ISPRS Journal of photogrammetry and remote sensing, vol 66 n° 2 (March - April 2011)
PermalinkICESat GLAS data for urban environment monitoring / P. Gong in IEEE Transactions on geoscience and remote sensing, vol 49 n° 3 (March 2011)
PermalinkQuality analysis on 3D building models reconstructed from airborne laser scanning data / Sander J. Elberink in ISPRS Journal of photogrammetry and remote sensing, vol 66 n° 2 (March - April 2011)
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