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Termes IGN > sciences humaines et sociales > sociologie > civilisation > culture
culture
Commentaire :
>> industrie culturelle, cultural studies, activité culturelle, civilisation, contribution au concept de culture, ethnologie, vie intellectuelle. >>Terme(s) spécifique(s) : cosmopolitisme, culture et handicapés, compte rendu, acculturation, anthropologie de l'éducation, biculturalisme, cognition et culture, communication et culture, communication interculturelle, conflit culturel, contre-culture, culture de masse, culture dominante, culture et jeunesse, culture juridique, culture personnelle, culture politique, culture populaire, diffusion de la culture, économie de la culture, éducation, équipement culturel, géographie culturelle, histoire des mentalités, humanisme, langage et culture, média et culture, patrimoine culturel, personnalité et culture, politique culturelle, politique et culture, psychanalyse et culture, relation culturelle, religion et culture, relativisme culturel, sémiotique et culture, sociologie de la culture, savoir et érudition, subculture. Source(s) : Grand Larousse universel. - Petit Robert 1. Equiv. LCSH : Culture. Voir aussi |
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Raster-based method for building selection in the multi-scale representation of two-dimensional maps / Yilang Shen in Geocarto international, vol 37 n° 22 ([10/10/2022])
[article]
Titre : Raster-based method for building selection in the multi-scale representation of two-dimensional maps Type de document : Article/Communication Auteurs : Yilang Shen, Auteur ; Tinghua Ai, Auteur ; Rong Zhao, Auteur Année de publication : 2022 Article en page(s) : pp 6494 - 6518 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] analyse de groupement
[Termes IGN] bâtiment
[Termes IGN] densité du bâti
[Termes IGN] distribution spatiale
[Termes IGN] données matricielles
[Termes IGN] représentation cartographique 2D
[Termes IGN] représentation multiple
[Termes IGN] segmentation
[Termes IGN] superpixel
[Termes IGN] triangulation de Delaunay
[Vedettes matières IGN] GénéralisationRésumé : (auteur) In the multi-scale representation of maps, a selection operation is usually applied to reduce the number of map elements and improve legibility while maintaining the original distribution characteristics. During the past few decades, many methods for vector building selection have been developed; however, pixel-based methods are relatively lacking. In this paper, a multiple-strategy method for raster building selection is proposed. In this method, to preserve the distribution range, a new homogeneous linear spectral clustering (HLSC) superpixel segmentation method is developed for the relatively homogeneous spatial division of building groups. Then, to preserve the relative distribution density, multi-level spatial division is performed according to the local number of buildings. Finally, to preserve the local geometric, attributive and geographical characteristics, four selection strategies, namely, the minimum centroid distance, minimum boundary distance, maximum area and considering geographical element strategies, are designed to generate selection results. To evaluate the proposed method, dispersed buildings in a suburban area are utilized to perform selection tasks. The experimental results indicate that the proposed method can effectively select dispersed irregular buildings at different levels of detail while maintaining the original distribution range and relative distribution density. In addition, the use of multiple selection strategies considering various geometric, attributive and geographical characteristics provides multiple options for cartography. Numéro de notice : A2022-727 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2021.1943007 Date de publication en ligne : 29/09/2021 En ligne : https://doi.org/10.1080/10106049.2021.1943007 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101673
in Geocarto international > vol 37 n° 22 [10/10/2022] . - pp 6494 - 6518[article]Identify urban building functions with multisource data: a case study in Guangzhou, China / Yingbin Deng in International journal of geographical information science IJGIS, vol 36 n° 10 (October 2022)
[article]
Titre : Identify urban building functions with multisource data: a case study in Guangzhou, China Type de document : Article/Communication Auteurs : Yingbin Deng, Auteur ; Renrong Chen, Auteur ; Yang Ji, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 2060 - 2085 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] approche hiérarchique
[Termes IGN] batiment commercial
[Termes IGN] bâtiment industriel
[Termes IGN] bâtiment public
[Termes IGN] Canton (Kouangtoung)
[Termes IGN] données multisources
[Termes IGN] empreinte
[Termes IGN] exploration de données
[Termes IGN] Extreme Gradient Machine
[Termes IGN] figure géométrique
[Termes IGN] image Gaofen
[Termes IGN] logement
[Termes IGN] point d'intérêt
[Termes IGN] zone urbaineRésumé : (auteur) Building function type is an important parameter for urban planning and disaster management. However, existing identification methods do not always correctly recognize all building functions because of missing point of interest (POI) data in private areas. In this study, we proposed a hierarchical data-mining model to identify building function types using accessible auxiliary data, which was then applied to a case study. Residential building property was assessed to address missing residential POIs. The building functions were assigned to one of five different types, or a mixed-function type. Standard deviation and mean values extracted from remotely sensed images, distances to major roads, and building shape parameters were used to infer the function types of buildings without assigned function types. The proposed model was able to identify 65% of buildings not previously assigned as residential through the POI, with an overall accuracy of 87%. In addition, all buildings were successfully assigned a function type of residential, commercial, office, warehouse, public service, or mixed-function, with an overall accuracy of 85% for unclassified buildings. Our results demonstrated that missing POI data in private areas could be addressed by integration with multisource data using a simple method. Numéro de notice : A2022-739 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2022.2046756 Date de publication en ligne : 07/03/2022 En ligne : https://doi.org/10.1080/13658816.2022.2046756 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101716
in International journal of geographical information science IJGIS > vol 36 n° 10 (October 2022) . - pp 2060 - 2085[article]Machine learning for spatial analyses in urban areas: a scoping review / Ylenia Casali in Sustainable Cities and Society, vol 85 (October 2022)
[article]
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]Semi-supervised adversarial recognition of refined window structures for inverse procedural façade modelling / Han Hu in ISPRS Journal of photogrammetry and remote sensing, vol 192 (October 2022)
[article]
Titre : Semi-supervised adversarial recognition of refined window structures for inverse procedural façade modelling Type de document : Article/Communication Auteurs : Han Hu, Auteur ; Xinrong Liang, Auteur ; Yulin Ding, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 215 - 231 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage profond
[Termes IGN] classification semi-dirigée
[Termes IGN] échantillonnage de données
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] façade
[Termes IGN] fenêtre (bâtiment)
[Termes IGN] modélisation 3D du bâti BIM
[Termes IGN] photographie aérienne oblique
[Termes IGN] réseau antagoniste génératifRésumé : (auteur) Deep learning methods are typically data-hungry and require many labelled samples. Unfortunately, the amount of effort required to label the data has significantly hindered the application of deep learning methods, especially in 3D modelling tasks requiring heterogeneous samples. This paper proposes a semi-supervised adversarial recognition strategy embedded in the inverse procedural modelling engine to reduce data annotation costs for learning to model 3D façades. Beginning with textured level-of-details models, we use convolutional neural networks to recognise the types and estimate the parameters of windows from image patches. The window types and parameters are then assembled into the procedural grammar. A simple procedural engine is built inside off-the-shelf 3D modelling software, producing fine-grained window geometries. To obtain a useful model from a few labelled samples, we leverage a generative adversarial network to train the feature extractor in a semi-supervised manner. The adversarial training strategy exploits the unlabelled data to stabilise the training phase. Experiments using publicly available façade image datasets reveal that the proposed methods can improve classification accuracy and parameter estimation by approximately 10% and 50%, respectively, under the same network structure. In addition, performance gains are more pronounced when testing against unseen data featuring different façade styles. Numéro de notice : A2022-666 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2022.08.014 Date de publication en ligne : 30/08/2022 En ligne : https://doi.org/10.1016/j.isprsjprs.2022.08.014 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101528
in ISPRS Journal of photogrammetry and remote sensing > vol 192 (October 2022) . - pp 215 - 231[article]An improved multi-task pointwise network for segmentation of building roofs in airborne laser scanning point clouds / Chaoquan Zhang in Photogrammetric record, vol 37 n° 179 (September 2022)
[article]
Titre : An improved multi-task pointwise network for segmentation of building roofs in airborne laser scanning point clouds Type de document : Article/Communication Auteurs : Chaoquan Zhang, Auteur ; Hongchao Fan, Auteur Année de publication : 2022 Article en page(s) : pp 260 - 284 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie
[Termes IGN] analyse de groupement
[Termes IGN] apprentissage profond
[Termes IGN] classification barycentrique
[Termes IGN] classification par réseau neuronal récurrent
[Termes IGN] données d'entrainement (apprentissage automatique)
[Termes IGN] fusion de données
[Termes IGN] Norvège
[Termes IGN] Ransac (algorithme)
[Termes IGN] segmentation sémantique
[Termes IGN] semis de points
[Termes IGN] toitRésumé : (auteur) Roof plane segmentation is an essential step in the process of 3D building reconstruction from airborne laser scanning (ALS) point clouds. The existing approaches either rely on human intervention to select the appropriate input parameters for different data-sets or they are not automatic and efficient. To tackle these issues, an improved multi-task pointwise network is proposed to simultaneously segment instances (that is, individual roof planes) and semantics (that is, groups of roof planes with similar geometric shapes) in point clouds. PointNet++ is used as a backbone network to extract robust features in the first step. The features from semantics branch are then added to the instance branch to facilitate the learning of instance embeddings. After that, a feature fusion module is added to the semantics branch to acquire more discriminative features from the backbone network. To increase the accuracy of semantic predictions, fused semantic features of the points belonging to the same instance are aggregated together. Finally, a mean-shift clustering algorithm is employed on instance embeddings to produce the instance predictions. Furthermore, a new roof data-set (called RoofNTNU) is established by taking ALS point clouds as training data for automatic and more general segmentation. Experiments on the new roof data-set show that the method achieves promising segmentation results: the mean precision (mPrec) of 96.2% for the instance segmentation task and mean accuracy (mAcc) of 94.4% for the semantic segmentation task. Numéro de notice : A2022-936 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1111/phor.12420 Date de publication en ligne : 13/07/2022 En ligne : https://doi.org/10.1111/phor.12420 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102682
in Photogrammetric record > vol 37 n° 179 (September 2022) . - pp 260 - 284[article]Deep learning–based monitoring sustainable decision support system for energy building to smart cities with remote sensing techniques / Wang Yue in Photogrammetric Engineering & Remote Sensing, PERS, vol 88 n° 9 (September 2022)PermalinkParcel Manager: A parcel reshaping model incorporating design rules of residential development / Maxime Colomb in Transactions in GIS, vol 26 n° 6 (September 2022)PermalinkThe cartography of Kallihirua?: Reassessing indigenous mapmaking and Arctic encounters / Peter R. Martin in Cartographica, vol 57 n° 3 (September 2022)PermalinkEstimating generalized measures of local neighbourhood context from multispectral satellite images using a convolutional neural network / Alex David Singleton in Computers, Environment and Urban Systems, vol 95 (July 2022)PermalinkExploring the vertical dimension of street view image based on deep learning: a case study on lowest floor elevation estimation / Huan Ning in International journal of geographical information science IJGIS, vol 36 n° 7 (juillet 2022)PermalinkGANmapper: geographical data translation / Abraham Noah Wu in International journal of geographical information science IJGIS, vol 36 n° 7 (juillet 2022)PermalinkGraph-based block-level urban change detection using Sentinel-2 time series / Nan Wang in Remote sensing of environment, vol 274 (June 2022)PermalinkAutomatic training data generation in deep learning-aided semantic segmentation of heritage buildings / Arnadi Murtiyoso in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-2-2022 (2022 edition)PermalinkART-RISK 3.0, a fuzzy-based platform that combine GIS and expert assessments for conservation strategies in cultural heritage / M. Moreno in Journal of Cultural Heritage, vol 55 (May - June 2022)PermalinkCity3D: Large-scale building reconstruction from airborne LiDAR point clouds / Jin Huang in Remote sensing, vol 14 n° 9 (May-1 2022)Permalink