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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)
[article]
Titre : Deep learning–based monitoring sustainable decision support system for energy building to smart cities with remote sensing techniques Type de document : Article/Communication Auteurs : Wang Yue, Auteur ; Changgang Yu, Auteur ; A. Antonidoss, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 593 - 601 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle
[Termes IGN] apprentissage profond
[Termes IGN] bâtiment
[Termes IGN] capteur (télédétection)
[Termes IGN] économie d'énergie
[Termes IGN] internet des objets
[Termes IGN] performance énergétique
[Termes IGN] réseau neuronal artificiel
[Termes IGN] système d'aide à la décision
[Termes IGN] ville durable
[Termes IGN] ville intelligenteRésumé : (auteur) In modern society, energy conservation is an important consideration for sustainability. The availability of energy-efficient infrastructures and utilities depend on the sustainability of smart cities. The big streaming data generated and collected by smart building devices and systems contain useful information that needs to be used to make timely action and better decisions. The ultimate objective of these procedures is to enhance the city's sustainability and livability. The replacement of decades-old infrastructures, such as underground wiring, steam pipes, transportation tunnels, and high-speed Internet installation, is already a major problem for major urban regions. There are still certain regions in big cities where broadband wireless service is not available. The decision support system is recently acquiring increasing attention in the smart city context. In this article, a deep learning–based sustainable decision support system (DLSDSS) has been proposed for energy building in smart cities. This study proposes the integration of the Internet of Things into smart buildings for energy management, utilizing deep learning methods for sensor information decision making. Building a socially advanced environment aims to enhance city services and urban administration for residents in smart cities using remote sensing techniques. The proposed deep learning methods classify buildings based on energy efficiency. Data gathered from the sensor network to plan smart cities' development include a deep learning algorithm's structural assembly of data. The deep learning algorithm provides decision makers with a model for the big data stream. The numerical results show that the proposed method reduces energy consumption and enhances sensor data accuracy by 97.67% with better decision making in planning smart infrastructures and services. The experimental outcome of the DLSDSS enhances accuracy (97.67%), time complexity (98.7%), data distribution rate (97.1%), energy consumption rate (98.2%), load shedding ratio (95.8%), and energy efficiency (95.4%). Numéro de notice : A2022-812 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.22-00010R2 Date de publication en ligne : 01/09/2022 En ligne : https://doi.org/10.14358/PERS.22-00010R2 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101972
in Photogrammetric Engineering & Remote Sensing, PERS > vol 88 n° 9 (September 2022) . - pp 593 - 601[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 105-2022091 SL Revue Centre de documentation Revues en salle Disponible Parcel Manager: A parcel reshaping model incorporating design rules of residential development / Maxime Colomb in Transactions in GIS, vol 26 n° 6 (September 2022)
[article]
Titre : Parcel Manager: A parcel reshaping model incorporating design rules of residential development Type de document : Article/Communication Auteurs : Maxime Colomb , Auteur ; Cécile Tannier, Auteur ; Julien Perret , Auteur ; Paul Chapron , Auteur ; Mickaël Brasebin , Auteur Année de publication : 2022 Projets : 3-projet - voir note / Article en page(s) : pp 2558 - 2597 Note générale : bibliographie
This research benefited from the financial support of the I-SITE University Bourgogne Franche-Comté as part of the project PubPrivLands.Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] bâtiment
[Termes IGN] code source libre
[Termes IGN] construction
[Termes IGN] densification
[Termes IGN] figure géométrique
[Termes IGN] habitat urbain
[Termes IGN] logiciel libre
[Termes IGN] parcelle cadastrale
[Termes IGN] règle d'urbanismeRésumé : (auteur) Parcels are the smallest units of land usually considered for urban development purposes. Analyses at the parcel level allow taking into account the administrative and physical environment of buildings with accuracy (roads, surrounding buildings, local design rules for building construction). In this article, we propose a free and open source software application, namely Parcel Manager, whose aims are twofold: (1) to assess the effects of different parcel reshaping rules and planning principles on the location, the number, and the shape of reshaped parcels; and (2) to provide a basis to assess the potential for new building construction. To this end, Parcel Manager simulates a large variety of parcel reshaping processes, with or without the joint creation of new roads, and produces realistic parcel layouts that represent either infill urban developments, edge expansions, or leapfrog developments. It can be used to determine if the densification of built parcels is feasible or not, regarding planning and design rules as well as the current urban fabric. The current version of Parcel Manager only concerns the reshaping of parcels dedicated to the construction of residential buildings but not other types of buildings. Numéro de notice : A2022-577 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Autre URL associée : vers HAL Thématique : GEOMATIQUE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12970 Date de publication en ligne : 03/08/2022 En ligne : https://doi.org/10.1111/tgis.12970 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101335
in Transactions in GIS > vol 26 n° 6 (September 2022) . - pp 2558 - 2597[article]Estimating 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)
[article]
Titre : Estimating generalized measures of local neighbourhood context from multispectral satellite images using a convolutional neural network Type de document : Article/Communication Auteurs : Alex David Singleton, Auteur ; Dani Arribas-Bel, Auteur ; John Murray, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 101802 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse de groupement
[Termes IGN] analyse en composantes principales
[Termes IGN] apprentissage automatique
[Termes IGN] bâtiment
[Termes IGN] carte d'occupation du sol
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] Grande-Bretagne
[Termes IGN] image multibande
[Termes IGN] image Sentinel-MSI
[Termes IGN] morphologie urbaine
[Termes IGN] pondération
[Termes IGN] processeur graphiqueRésumé : (auteur) The increased availability of high-resolution multispectral imagery captured by remote sensing platforms provides new opportunities for the characterisation and differentiation of urban context. The discovery of generalized latent representations from such data are however under researched within the social sciences. As such, this paper exploits advances in machine learning to implement a new method of capturing measures of urban context from multispectral satellite imagery at a very small area level through the application of a convolutional autoencoder (CAE). The utility of outputs from the CAE is enhanced through the application of spatial weighting, and the smoothed outputs are then summarised using cluster analysis to generate a typology comprising seven groups describing salient patterns of differentiated urban context. The limits of the technique are discussed with reference to the resolution of the satellite data utilised within the study and the interaction between the geography of the input data and the learned structure. The method is implemented within the context of Great Britain, however, is applicable to any location where similar high resolution multispectral imagery are available. Numéro de notice : A2022-370 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article DOI : 10.1016/j.compenvurbsys.2022.101802 Date de publication en ligne : 19/04/2022 En ligne : https://doi.org/10.1016/j.compenvurbsys.2022.101802 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100606
in Computers, Environment and Urban Systems > vol 95 (July 2022) . - n° 101802[article]Exploring 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)
[article]
Titre : Exploring the vertical dimension of street view image based on deep learning: a case study on lowest floor elevation estimation Type de document : Article/Communication Auteurs : Huan Ning, Auteur ; Zhenlong Li, Auteur ; Xinyue Ye, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 1317 - 1342 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage profond
[Termes IGN] détection d'objet
[Termes IGN] distorsion d'image
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] hauteur du bâti
[Termes IGN] image Streetview
[Termes IGN] lever tachéométrique
[Termes IGN] modèle numérique de surface
[Termes IGN] porteRésumé : (auteur) Street view imagery such as Google Street View is widely used in people’s daily lives. Many studies have been conducted to detect and map objects such as traffic signs and sidewalks for urban built-up environment analysis. While mapping objects in the horizontal dimension is common in those studies, automatic vertical measuring in large areas is underexploited. Vertical information from street view imagery can benefit a variety of studies. One notable application is estimating the lowest floor elevation, which is critical for building flood vulnerability assessment and insurance premium calculation. In this article, we explored the vertical measurement in street view imagery using the principle of tacheometric surveying. In the case study of lowest floor elevation estimation using Google Street View images, we trained a neural network (YOLO-v5) for door detection and used the fixed height of doors to measure doors’ elevation. The results suggest that the average error of estimated elevation is 0.218 m. The depthmaps of Google Street View were utilized to traverse the elevation from the roadway surface to target objects. The proposed pipeline provides a novel approach for automatic elevation estimation from street view imagery and is expected to benefit future terrain-related studies for large areas. Numéro de notice : A2022-465 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2021.1981334 Date de publication en ligne : 06/10/2021 En ligne : https://doi.org/10.1080/13658816.2021.1981334 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100970
in International journal of geographical information science IJGIS > vol 36 n° 7 (juillet 2022) . - pp 1317 - 1342[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 079-2022071 SL Revue Centre de documentation Revues en salle Disponible GANmapper: 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)PermalinkNavigation network derivation for QR code-based indoor pedestrian path planning / Jinjin Yan in Transactions in GIS, vol 26 n° 3 (May 2022)PermalinkDetermination of building flood risk maps from LiDAR mobile mapping data / Yu Feng in Computers, Environment and Urban Systems, vol 93 (April 2022)PermalinkA GAN-based approach toward architectural line drawing colorization prototyping / Qian (Chayn) Sun in The Visual Computer, vol 38 n° 4 (April 2022)PermalinkAutomated 3D reconstruction of LoD2 and LoD1 models for All 10 million buildings of the Netherlands / Ravi Peters in Photogrammetric Engineering & Remote Sensing, PERS, vol 88 n° 3 (March 2022)PermalinkSimulating fire-safe cities using a machine learning-based algorithm for the complex urban forms of developing nations: a case of Mumbai India / Vaibhav Kumar in Geocarto international, vol 37 n° 4 ([15/02/2022])Permalink