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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)
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]
Titre : City-descriptive input data for urban climate models: Model requirements, data sources and challenges Type de document : Article/Communication Auteurs : Valéry Masson, Auteur ; Wieke Heldens, Auteur ; Erwan Bocher, Auteur ; Marion Bonhomme, Auteur ; Bénédicte Bucher Année de publication : 2020 Projets : URCLIM / Masson, Valéry Article en page(s) : n° 100536 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes descripteurs IGN] arbre urbain
[Termes descripteurs IGN] données localisées numériques
[Termes descripteurs IGN] données socio-économiques
[Termes descripteurs IGN] flore urbaine
[Termes descripteurs IGN] morphologie urbaine
[Termes descripteurs IGN] occupation du sol
[Termes descripteurs IGN] ville
[Termes descripteurs IGN] zone urbaineRésumé : (auteur) Cities are particularly vulnerable to meteorological hazards because of the concentration of population, goods, capital stock and infrastructure. Urban climate services require multi-disciplinary and multi-sectorial approaches and new paradigms in urban climate modelling. This paper classifies the required urban input data for both mesoscale state-of-the-art Urban Canopy Models (UCMs) and microscale Obstacle Resolving Models (ORM) into five categories and reviews the ways in which they can be obtained. The first two categories are (1) land cover, and (2) building morphology. These govern the main interactions between the city and the urban climate and the Urban Heat Island. Interdependence between morphological parameters and UCM geometric hypotheses are discussed. Building height, plan and wall area densities are recommended as the main input variables for UCMs, whereas ORMs require 3D building data. Recently, three other categories of urban data became relevant for finer urban studies and adaptation to climate change: (3) building design and architecture, (4) building use, anthropogenic heat and socio-economic data, and (5) urban vegetation data. Several methods for acquiring spatial information are reviewed, including remote sensing, geographic information system (GIS) processing from administrative cadasters, expert knowledge and crowdsourcing. Data availability, data harmonization, costs/efficiency trade-offs and future challenges are then discussed. Numéro de notice : A2020-003 Affiliation des auteurs : LaSTIG+Ext (2016-2019) Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.uclim.2019.100536 date de publication en ligne : 19/11/2019 En ligne : https://doi.org/10.1016/j.uclim.2019.100536 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94290
in Urban climate > vol 31 (March 2020) . - n° 100536[article]
Titre : Morphological tessellation as a way of partitioning space: Improving consistency in urban morphology at the plot scale Type de document : Article/Communication Auteurs : Martin Fleischmann, Auteur ; Alessandra Feliciotti, Auteur ; Ombretta Romice, Auteur ; Sergio Porta, Auteur Année de publication : 2020 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes descripteurs IGN] bati
[Termes descripteurs IGN] empreinte
[Termes descripteurs IGN] information géographique
[Termes descripteurs IGN] morphologie urbaine
[Termes descripteurs IGN] morphométrie
[Termes descripteurs IGN] parcelle cadastrale
[Termes descripteurs IGN] tessellation
[Termes descripteurs IGN] Zurich (Suisse)Résumé : (auteur) Urban Morphometrics (UMM) is an expanding area of urban studies that aims at representing and measuring objectively the physical form of cities to support evidence-based research. An essential step in its development is the identification of a suitable spatial unit of analysis, where suitability is determined by its degree of reliability, universality, accessibility and significance in capturing essential urban form patterns. In Urban Morphology such unit is found in the plot, a fundamental component in the morphogenetic of urban settlements. However, the plot is a conceptually and analytically ambiguous concept and a kind of spatial information often unavailable or inconsistently represented across geographies, issues that limit its reliability and universality and hence its suitability for Urban Morphometric applications. This calls for alternative methods of deriving a spatial unit able to convey reliable plot-scale information, possibly comparable with that provided by plots. This paper presents Morphological Tessellation (MT), an objectively and universally applicable method that derives a spatial unit named Morphological Cell (MC) from widely available data on building footprint only and tests its informational value as proxy data in capturing plot-scale spatial properties of urban form. Using the city of Zurich (CH) as case study we compare MT to the cadastral layer on a selection of morphometric characters capturing different geometrical and configurational properties of urban form, to test the degree of informational similarity between MT and cadastral plots. Findings suggest that MT can be considered an efficient informational proxy for cadastral plots for many of the tested morphometric characters, that there are kinds of plot-scale information only plots can provide, as well as kinds only morphological tessellation can provide. Overall, there appears to be clear scope for application of MT as fundamental spatial unit of analysis in Urban Morphometrics, opening the way to large-scale urban morphometric analysis. Numéro de notice : A2020-192 Thématique : GEOMATIQUE Nature : Article DOI : 10.1016/j.compenvurbsys.2019.101441 date de publication en ligne : 23/11/2019 En ligne : https://doi.org/10.1016/j.compenvurbsys.2019.101441 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94854
in Computers, Environment and Urban Systems > vol 80 (March 2020)[article]
Titre : Extending Processing Toolbox for assessing the logical consistency of OpenStreetMap data Type de document : Article/Communication Auteurs : Sukhjit Singh Sehra, Auteur ; Jaiteg Singh, Auteur ; Hardeep Singh Rai, Auteur Année de publication : 2020 Article en page(s) : pp 44 - 71 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web
[Termes descripteurs IGN] cartographie collaborative
[Termes descripteurs IGN] données localisées
[Termes descripteurs IGN] données localisées des bénévoles
[Termes descripteurs IGN] données localisées libres
[Termes descripteurs IGN] données routières
[Termes descripteurs IGN] Inde
[Termes descripteurs IGN] information sémantique
[Termes descripteurs IGN] intégrité topologique
[Termes descripteurs IGN] morphologie urbaine
[Termes descripteurs IGN] OpenStreetMap
[Termes descripteurs IGN] QGIS
[Termes descripteurs IGN] qualité des donnéesRésumé : (auteur) OpenStreetMap (OSM) produces a huge amount of labeled spatial data, but its quality has always been a deep concern. Numerous quality issues have been discussed in the vast literature, while the fitness of OSM for road navigability is only partly explored. Navigability depends on logical consistency, which focuses on the existence of logical contradictions within a data set. Researchers have discussed the insufficiency of established methods and the lack of a computational paradigm to assess the quality of the OSM data. To address the research gaps, the current work extended the capabilities of the Quantum GIS Processing Toolbox for assessment of spatial data. The models and scripts developed are able to assess logical consistency based on geographical topological consistency, semantic information, and morphological consistency. The established and proxy indicators are selected for measuring the logical consistency of OSM data for navigability. For empirical validation, OSM Punjab data are compared with authoritative data from HERE (proprietary) and the Remote Sensing Centre (RSC), Punjab, India. The results conclude that even the proprietary road data sets are not free from logical inconsistencies and data contributed by the masses are credible and navigable. OSM has produced better results than the RSC, but needs more crowd contributions to improve its quality. Numéro de notice : A2020-101 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12587 date de publication en ligne : 08/11/2019 En ligne : https://doi.org/10.1111/tgis.12587 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94692
in Transactions in GIS > Vol 24 n° 1 (February 2020) . - pp 44 - 71[article]
Titre : Co-visualization of air temperature and urban data for visual exploration Type de document : Article/Communication Auteurs : Jacques Gautier Congrès : Congrès: IEEE VIS 2020, (VAST, INFOVIS, SCIVIS) premier forum for advances in visualization and visual analytics (25 - 30 October 2020; Virtuel) Editeur : New-York : IEEE Computer society Année de publication : 2020 Projets : URCLIM / Masson, Valéry Importance : 5 p. Format : 21 x 30 cm Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes descripteurs IGN] analyse géovisuelle
[Termes descripteurs IGN] distribution spatiale
[Termes descripteurs IGN] exploration de données géographiques
[Termes descripteurs IGN] ilot thermique urbain
[Termes descripteurs IGN] modèle 3D de l'espace urbain
[Termes descripteurs IGN] morphologie urbaine
[Termes descripteurs IGN] rendu (géovisualisation)
[Termes descripteurs IGN] représentation graphique
[Termes descripteurs IGN] température de l'air
[Termes descripteurs IGN] visualisation 3D
[Vedettes matières IGN] GéovisualisationRésumé : (auteur) Urban climate data remain complex to analyze regarding their spatial distribution. The co-visualization of simulated air temperature into urban models could help experts to analyze horizontal and vertical spatial distributions. We design a co-visualization framework enabling simulated air temperature data exploration, based on the graphic representation of three types of geometric proxies, and their co-visualization with a 3D urban model with various possible rendering styles. Through this framework, we aim at allowing meteorological researchers to visually analyze and interpret the relationships between simulated air temperature data and urban morphology. Numéro de notice : C2020-005 Affiliation des auteurs : LaSTIG (2020- ) Thématique : GEOMATIQUE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : en attente Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96161 Documents numériques
en open access
Co-visualization... - pdf auteur -Adobe Acrobat PDFQGIS in Remote Sensing, Volume 3. QGIS and Applications in Territorial Planning / Nicolas Baghdadi (2018)
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