Descripteur
Documents disponibles dans cette catégorie (144)
Ajouter le résultat dans votre panier
Visionner les documents numériques
Affiner la recherche Interroger des sources externes
Etendre la recherche sur niveau(x) vers le bas
Generation of concise 3D building model from dense meshes by extracting and completing planar primitives / Xinyi Liu in Photogrammetric record, vol 38 n° 181 (March 2023)
[article]
Titre : Generation of concise 3D building model from dense meshes by extracting and completing planar primitives Type de document : Article/Communication Auteurs : Xinyi Liu, Auteur ; Xianzhang Zhu, Auteur ; Yongjun Zhang, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : pp 22 - 46 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie
[Termes IGN] adjacence
[Termes IGN] bati
[Termes IGN] maillage
[Termes IGN] modélisation 3D du bâti BIM
[Termes IGN] modélisation du bâti
[Termes IGN] primitive géométrique
[Termes IGN] reconstruction 3D
[Termes IGN] segmentation en plan
[Termes IGN] semis de pointsRésumé : (auteur) The generation of a concise building model has been and continues to be a challenge in photogrammetry and computer graphics. The current methods typically focus on the simplicity and fidelity of the model, but those methods either fail to preserve the structural information or suffer from low computational efficiency. In this paper, we propose a novel method to generate concise building models from dense meshes by extracting and completing the planar primitives of the building. From the perspective of probability, we first extract planar primitives from the input mesh and obtain the adjacency relationships between the primitives. Since primitive loss and structural defects are inevitable in practice, we employ a novel structural completion approach to eliminate linkage errors. Finally, the concise polygonal mesh is reconstructed by connectivity-based primitive assembling. Our method is efficient and robust to various challenging data. Experiments on various building models revealed the efficacy and applicability of our method. Numéro de notice : A2023-162 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1111/phor.12438 Date de publication en ligne : 04/01/2023 En ligne : https://doi.org/10.1111/phor.12438 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102865
in Photogrammetric record > vol 38 n° 181 (March 2023) . - pp 22 - 46[article]Comparative analysis of different CNN models for building segmentation from satellite and UAV images / Batuhan Sariturk in Photogrammetric Engineering & Remote Sensing, PERS, vol 89 n° 2 (February 2023)
[article]
Titre : Comparative analysis of different CNN models for building segmentation from satellite and UAV images Type de document : Article/Communication Auteurs : Batuhan Sariturk, Auteur ; Damla Kumbasar, Auteur ; Dursun Zafer Seker, Auteur Année de publication : 2023 Article en page(s) : pp 97 - 105 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse comparative
[Termes IGN] bati
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] données d'entrainement (apprentissage automatique)
[Termes IGN] image captée par drone
[Termes IGN] image satellite
[Termes IGN] segmentation sémantiqueRésumé : (auteur) Building segmentation has numerous application areas such as urban planning and disaster management. In this study, 12 CNN models (U-Net, FPN, and LinkNet using EfficientNet-B5 backbone, U-Net, SegNet, FCN, and six Residual U-Net models) were generated and used for building segmentation. Inria Aerial Image Labeling Data Set was used to train models, and three data sets (Inria Aerial Image Labeling Data Set, Massachusetts Buildings Data Set, and Syedra Archaeological Site Data Set) were used to evaluate trained models. On the Inria test set, Residual-2 U-Net has the highest F1 and Intersection over Union (IoU) scores with 0.824 and 0.722, respectively. On the Syedra test set, LinkNet-EfficientNet-B5 has F1 and IoU scores of 0.336 and 0.246. On the Massachusetts test set, Residual-4 U-Net has F1 and IoU scores of 0.394 and 0.259. It has been observed that, for all sets, at least two of the top three models used residual connections. Therefore, for this study, residual connections are more successful than conventional convolutional layers. Numéro de notice : A2023-143 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.22-00084R2 Date de publication en ligne : 01/02/2023 En ligne : https://doi.org/10.14358/PERS.22-00084R2 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102718
in Photogrammetric Engineering & Remote Sensing, PERS > vol 89 n° 2 (February 2023) . - pp 97 - 105[article]Coupling graph deep learning and spatial-temporal influence of built environment for short-term bus travel demand prediction / Tianhong Zhao in Computers, Environment and Urban Systems, vol 94 (June 2022)
[article]
Titre : Coupling graph deep learning and spatial-temporal influence of built environment for short-term bus travel demand prediction Type de document : Article/Communication Auteurs : Tianhong Zhao, Auteur ; Zhengdong Huang, Auteur ; Wei Tu, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 101776 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] apprentissage profond
[Termes IGN] bati
[Termes IGN] données spatiotemporelles
[Termes IGN] gestion de trafic
[Termes IGN] graphe
[Termes IGN] logement
[Termes IGN] migration pendulaire
[Termes IGN] modèle de simulation
[Termes IGN] régression géographiquement pondérée
[Termes IGN] service public
[Termes IGN] Shenzhen
[Termes IGN] système de transport intelligent
[Termes IGN] transport public
[Termes IGN] transport urbainRésumé : (auteur) Accurate and robust short-term bus travel prediction facilitates operating the bus fleet to provide comfortable and flexible bus services. The built environment, including land use, buildings, and public facilities, has an important influence on bus travel demand prediction. However, previous studies regarded the built environment as a static feature thus even ignored its influence on bus travel in deep learning framework. To fill this gap, we propose a graph deep learning-based approach coupling with spatiotemporal influence of built environment (GDLBE) to enhance short-term bus travel demand prediction. A time-dependent geographically weighted regression method is used to resolve the dynamic influence of the built environment on bus travel demand at different times of the day. A graph deep learning module is used to capture the comprehensive spatial and temporal dependency behind massive bus travel demand. The short-term bus travel demand is predicted by fusing the dynamic built environment influences and spatiotemporal dependency. An experiment in Shenzhen is conducted to evaluate the performance of the proposed approach. Baseline methods are compared, and the results demonstrate that the proposed approach outperforms the baselines. These results will help bus fleet dispatch for smart transportation. Numéro de notice : A2022-245 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1016/j.compenvurbsys.2022.101776 Date de publication en ligne : 12/03/2022 En ligne : https://doi.org/10.1016/j.compenvurbsys.2022.101776 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100185
in Computers, Environment and Urban Systems > vol 94 (June 2022) . - n° 101776[article]Exploring the association between street built environment and street vitality using deep learning methods / Yunqin Li in Sustainable Cities and Society, vol 79 (April 2022)
[article]
Titre : Exploring the association between street built environment and street vitality using deep learning methods Type de document : Article/Communication Auteurs : Yunqin Li, Auteur ; Nobuyoshi Yabuki, Auteur ; Tomohiro Fukuda, Auteur Année de publication : 2022 Article en page(s) : n° 103656 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG
[Termes IGN] apprentissage profond
[Termes IGN] attractivité (aménagement)
[Termes IGN] bati
[Termes IGN] image Streetview
[Termes IGN] Japon
[Termes IGN] morphologie urbaine
[Termes IGN] OpenStreetMap
[Termes IGN] piéton
[Termes IGN] planification urbaine
[Termes IGN] processus de hiérarchisation analytique
[Termes IGN] régression linéaire
[Termes IGN] scène urbaine
[Termes IGN] segmentation sémantique
[Termes IGN] système d'information géographique
[Termes IGN] urbanisme
[Termes IGN] ville intelligenteRésumé : (auteur) Street vitality has become an essential indicator for evaluating the attractiveness and potential of the sustainable development of urban blocks, and it can be reflected by the type and the frequency of people's pedestrian activities on the street. While it is recognized that street built environment features affect pedestrian behavior and street vitality, quantifying the impact of these characteristics remains inconclusive. This paper proposes an automated deep learning approach to quantitatively explore the association between the street built environment and street vitality. First, we established a deep learning model for street vitality classification for automatic evaluation of street vitality based on the volumes and activities of pedestrians in the street through multiple object tracking and scene classification. Then, we applied semantic segmentation to measure five selected vitality-related street built environment variables. Finally, a linear regression model was applied to evaluate the built environment variables’ significance and effects on street vitality. To verify our method's accuracy and applicability, we selected a commercial complex in Osaka as an illustrative example. The experimental results highlight that street width and transparency have significant positive effects on street vitality. Compared with traditional methods, our approach is feasible, reliable, transferable, and more efficient. Numéro de notice : A2022-266 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/URBANISME Nature : Article DOI : 10.1016/j.scs.2021.103656 Date de publication en ligne : 10/01/2022 En ligne : https://doi.org/10.1016/j.scs.2021.103656 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100271
in Sustainable Cities and Society > vol 79 (April 2022) . - n° 103656[article]Multi-criteria geographic analysis for automated cartographic generalization / Guillaume Touya in Cartographic journal (the), vol 59 n° 1 (February 2022)
[article]
Titre : Multi-criteria geographic analysis for automated cartographic generalization Type de document : Article/Communication Auteurs : Guillaume Touya , Auteur Année de publication : 2022 Projets : 1-Pas de projet / Article en page(s) : pp 18 - 34 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] analyse multicritère
[Termes IGN] analyse spatiale
[Termes IGN] bati
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] densité du bâti
[Termes IGN] Electre
[Termes IGN] généralisation cartographique automatisée
[Vedettes matières IGN] GénéralisationMots-clés libres : PROMETHEE Résumé : (auteur) Cartographic generalization is a process similar to text summarization that transforms a map when scale is reduced. Cartographic generalization simplifies the map content while preserving as much as possible the initial characteristics and spatial relations of the map. The automation of this process requires a deep understanding of the context of each map feature, which involves different criteria such as the shape of the feature, the semantic nature of the feature, or the spatial patterns of its neighbouring features. This is why multiple criteria decision techniques can be relevant during the process. This paper proposes two use cases of cartographic generalization sub-tasks where multiple criteria decision techniques improve past techniques: the classification of urban building blocks and the ordering of the buildings to keep in the map as a priority. In both use cases, the proposed methods are experimented on large real cartographic datasets, and evaluated in comparison to alternative techniques. Numéro de notice : A2022-075 Affiliation des auteurs : UGE-LASTIG (2020- ) Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/00087041.2020.1858608 Date de publication en ligne : 26/07/2021 En ligne : https://doi.org/10.1080/00087041.2020.1858608 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98217
in Cartographic journal (the) > vol 59 n° 1 (February 2022) . - pp 18 - 34[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 030-2022011 RAB Revue Centre de documentation En réserve L003 Disponible Representing vector geographic information as a tensor for deep learning based map generalisation / Azelle Courtial (2022)PermalinkTowards efficient indoor/outdoor registration using planar polygons / Rahima Djahel in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-2-2021 (July 2021)PermalinkDetecting ground deformation in the built environment using sparse satellite InSAR data with a convolutional neural network / Nantheera Anantrasirichai in IEEE Transactions on geoscience and remote sensing, vol 59 n° 4 (April 2021)PermalinkGridded population mapping for Germany based on building density, height and type from Earth Observation data using census disaggregation and bottom-up estimates / Franz Schug in Plos one, vol 16 n° 3 (March 2021)PermalinkAn efficient representation of 3D buildings: application to the evaluation of city models / Oussama Ennafii (2021)PermalinkPermalinkA regression model of spatial accuracy prediction for Openstreetmap buildings / Ibrahim Maidaneh Abdi in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-4-2020 (August 2020)PermalinkSemCity Toulouse: a benchmark for building instance segmentation in satellite images / Ribana Roscher in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-5-2020 (August 2020)PermalinkExtraction of built-up areas from Landsat-8 OLI data based on spectral-textural information and feature selection using support vector machine method / Vijendra Singh Bramhe in Geocarto international, vol 35 n° 10 ([01/08/2020])PermalinkExtraction of urban built-up areas from nighttime lights using artificial neural network / Tingting Xu in Geocarto international, vol 35 n° 10 ([01/08/2020])PermalinkDense stereo matching strategy for oblique images that considers the plane directions in urban areas / Jianchen Liu in IEEE Transactions on geoscience and remote sensing, vol 58 n° 7 (July 2020)PermalinkMoGUS, un outil de modélisation et d'analyse comparative des trames urbaines / Dominique Badariotti in Revue internationale de géomatique, vol 30 n° 3-4 (juillet - décembre 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)PermalinkMapping urban grey and green structures for liveable cities using a 3D enhanced OBIA approach and vital statistics / E. Banzhaf in Geocarto international, vol 35 n° 6 ([01/05/2020])PermalinkMethod for extraction of airborne LiDAR point cloud buildings based on segmentation / Maohua Liu in Plos one, vol 15 n° 5 (May 2020)PermalinkMorphological tessellation as a way of partitioning space: Improving consistency in urban morphology at the plot scale / Martin Fleischmann in Computers, Environment and Urban Systems, vol 80 (March 2020)PermalinkImproving public data for building segmentation from Convolutional Neural Networks (CNNs) for fused airborne lidar and image data using active contours / David Griffiths in ISPRS Journal of photogrammetry and remote sensing, vol 154 (August 2019)PermalinkA graph-based approach for the structural analysis of road and building layouts / Mathieu Domingo in Geo-spatial Information Science, vol 22 n° 1 (March 2019)PermalinkAnalyse de données d’OpenStreetMap en vue de discriminer l’usage du sol lié au bâti / Jocelyn Le Maître (2019)PermalinkCorrecting rural building annotations in OpenStreetMap using convolutional neural networks / John E. Vargas-Muñoz in ISPRS Journal of photogrammetry and remote sensing, vol 147 (January 2019)PermalinkAn algorithm for on-the-fly K shortest paths finding in multi-storey buildings using a hierarchical topology model / Rosen Ivanov in International journal of geographical information science IJGIS, vol 32 n° 11-12 (November - December 2018)PermalinkOn the spatial distribution of buildings for map generalization / Zhiwei Wei in Cartography and Geographic Information Science, Vol 45 n° 6 (November 2018)PermalinkA voxel- and graph-based strategy for segmenting man-made infrastructures using perceptual grouping laws: comparison and evaluation / Yusheng Xu in Photogrammetric Engineering & Remote Sensing, PERS, vol 84 n° 6 (juin 2018)PermalinkRecognition of building group patterns in topographic maps based on graph partitioning and random forest / Xianjin He in ISPRS Journal of photogrammetry and remote sensing, vol 136 (February 2018)PermalinkFusion tardive d’images SPOT-6/7 et de données multitemporelles Sentinel-2 pour la détection de la tache urbaine / Cyril Wendl (2018)PermalinkLocalisation par l'image en milieu urbain : application à la réalité augmentée / Antoine Fond (2018)PermalinkPermalinkSimulation 3D de la constructibilité et utilisations pour l’aménagement [diaporama] / Mickaël Brasebin (2018)PermalinkVerification and updating of the database of topographic objects with geometric information about buildings by means of airborne laser scanning dataeans of Airborne Laser Scanning Data / Małgorzata Mendela-Anzlik in Reports on geodesy and geoinformatics, vol 103 n° 1 (June 2017)PermalinkToward optimum fusion of thermal hyperspectral and visible images in classification of urban area / Farhad Samadzadegan in Photogrammetric Engineering & Remote Sensing, PERS, vol 83 n° 4 (April 2017)PermalinkContinuously generalizing buildings to built-up areas by aggregating and growing / Dongliang Peng (2017)PermalinkRedistribution population data across a regular spatial grid according to buildings characteristics / Beata Calka in Geodesy and cartography, vol 65 n° 2 (December 2016)PermalinkEnhancing building footprints with squaring operations / Imran Lokhat in Journal of Spatial Information Science, JoSIS, n° 13 (September 2016)PermalinkNovel tool for examination of data completeness based on a comparative study of VGI data and official building datasets / Joanna Nowak Da Costa in Geodetski vestnik, vol 60 n° 3 (September - November 2016)PermalinkThe impacts of building orientation on polarimetric orientation angle estimation and model-based decomposition for multilook polarimetric SAR data in urban areas / Hongzhong Li in IEEE Transactions on geoscience and remote sensing, vol 54 n° 9 (September 2016)PermalinkDie Fortführung des 3D-Gebäudemodells LoD2 in Nordrhein-Westfalen / Marco Oestereich in ZFV, Zeitschrift für Geodäsie, Geoinformation und Landmanagement, Vol 141 n° 3 (Mai - Juni 2016)PermalinkLes données géographiques 3D pour simuler l'impact de la réglementation urbaine sur la morphologie du bâti / Mickaël Brasebin in Cartes & Géomatique, n° 227 (mars - mai 2016)PermalinkPermalinkConception et implémentation d'un modèle de style adapté à une application web 3D / Anouk Vinesse (2016)PermalinkTowards a generic method for building-parcel vector data adjustment by least squares / Yann Méneroux in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol II-3 W5 (October 2015)PermalinkCastles in the sky / Krista Montgomery in GEO: Geoconnexion international, vol 14 n° 6 (June 2015)PermalinkMeasuring thermal expansion using X-band persistent scatterer interferometry / Michele Crosetto in ISPRS Journal of photogrammetry and remote sensing, vol 100 (February 2015)PermalinkSpatio-temporal building population estimation for highly urbanized areas using GIS: spatio-temporal building population estimation / Konstantin Greger in Transactions in GIS, vol 19 n° 1 (February 2015)PermalinkCharacterisation of building alignments with new measures using C4.5 decision tree algorithm / Sinan Cetinkaya in Geodetski vestnik, vol 58 n° 3 ([01/09/2014])PermalinkSimuler les évolutions urbaines à l'aide de données géographiques urbaines 3D / Mickaël Brasebin in Revue internationale de géomatique, vol 24 n° 2 (juin - août 2014)Permalink