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Point cloud data processing optimization in spectral and spatial dimensions based on multispectral Lidar for urban single-wood extraction / Shuo Shi in ISPRS International journal of geo-information, vol 12 n° 3 (March 2023)
[article]
Titre : Point cloud data processing optimization in spectral and spatial dimensions based on multispectral Lidar for urban single-wood extraction Type de document : Article/Communication Auteurs : Shuo Shi, Auteur ; Xingtao Tang, Auteur ; Bowen Chen, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : n° 90 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] analyse spectrale
[Termes IGN] arbre urbain
[Termes IGN] détection d'objet
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] Houston (Texas)
[Termes IGN] interpolation
[Termes IGN] réflectance spectrale
[Termes IGN] segmentation
[Termes IGN] semis de pointsRésumé : (auteur) Lidar can effectively obtain three-dimensional information on ground objects. In recent years, lidar has developed rapidly from single-wavelength to multispectral hyperspectral imaging. The multispectral airborne lidar Optech Titan is the first commercial system that can collect point cloud data on 1550, 1064, and 532 nm channels. This study proposes a method of point cloud segmentation in the preprocessed intensity interpolation process to solve the problem of inaccurate intensity at the boundary during point cloud interpolation. The entire experiment consists of three steps. First, a multispectral lidar point cloud is obtained using point cloud segmentation and intensity interpolation; the spatial dimension advantage of the multispectral point cloud is used to improve the accuracy of spectral information interpolation. Second, point clouds are divided into eight categories by constructing geometric information, spectral reflectance information, and spectral characteristics. Accuracy evaluation and contribution analysis are also conducted through point cloud truth value and classification results. Lastly, the spatial dimension information is enhanced by point cloud drop sampling, the method is used to solve the error caused by airborne scanning and single-tree extraction of urban trees. Classification results showed that point cloud segmentation before intensity interpolation can effectively improve the interpolation and classification accuracies. The total classification accuracy of the data is improved by 3.7%. Compared with the extraction result (377) of single wood without subsampling treatment, the result of the urban tree extraction proved the effectiveness of the proposed method with a subsampling algorithm in improving the accuracy. Accordingly, the problem of over-segmentation is solved, and the final single-wood extraction result (329) is markedly consistent with the real situation of the region. Numéro de notice : A2023-159 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi12030090 Date de publication en ligne : 23/02/2023 En ligne : https://doi.org/10.3390/ijgi12030090 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102852
in ISPRS International journal of geo-information > vol 12 n° 3 (March 2023) . - n° 90[article]Programme LiDAR HD : vers une nouvelle cartographie 3D du territoire / Terry Moreau in XYZ, n° 174 (mars 2023)
[article]
Titre : Programme LiDAR HD : vers une nouvelle cartographie 3D du territoire Type de document : Article/Communication Auteurs : Terry Moreau, Auteur ; Hélène Buissart, Auteur ; Arnaud Allgeyer, Auteur ; Sofiane Kriat , Auteur ; Pierre-Yves Decavele, Auteur ; Romuald Dore, Auteur ; Gabrielle Roy, Auteur Année de publication : 2023 Article en page(s) : pp 45 - 49 Langues : Français (fre) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] acquisition de données
[Termes IGN] cartographie 3D
[Termes IGN] diffusion de données
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] France métropolitaine
[Termes IGN] QGIS
[Termes IGN] semis de points
[Termes IGN] système d'information géographique
[Termes IGN] télémétrie laser aéroportéRésumé : (Auteur) Le programme LiDAR HD est d’une ampleur inédite et porte un objectif ambitieux : acquérir des données LiDAR haute densité (HD) sur l’ensemble du territoire métropolitain et ultramarin (hors Guyane) pour en proposer la description 3D la plus fine jamais établie à l’échelle France entière. L’IGN coordonne ce programme et s’emploie à soutenir tous les usages de ce géocommun en devenir. Numéro de notice : A2023-171 Affiliation des auteurs : IGN (2020- ) Thématique : GEOMATIQUE/IMAGERIE Nature : Article nature-HAL : ArtSansCL DOI : sans Date de publication en ligne : 01/03/2023 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102856
in XYZ > n° 174 (mars 2023) . - pp 45 - 49[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 112-2023011 RAB Revue Centre de documentation En réserve L003 Disponible Provenance in GIServices: A semantic web approach / Zhaoyan Wu in ISPRS International journal of geo-information, vol 12 n° 3 (March 2023)
[article]
Titre : Provenance in GIServices: A semantic web approach Type de document : Article/Communication Auteurs : Zhaoyan Wu, Auteur ; Hao Li, Auteur ; Peng Yue, Auteur Année de publication : 2023 Article en page(s) : n° 118 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web
[Termes IGN] données localisées
[Termes IGN] métadonnées
[Termes IGN] ontologie
[Termes IGN] OWL
[Termes IGN] service web
[Termes IGN] service web sémantique
[Termes IGN] source de données
[Termes IGN] système d'information géographique
[Termes IGN] web sémantiqueRésumé : (auteur) Recent developments in Web Service and Semantic Web technologies have shown great promise for the automatic chaining of geographic information services (GIService), which can derive user-specific information and knowledge from large volumes of data in the distributed information infrastructure. In order for users to have an informed understanding of products generated automatically by distributed GIServices, provenance information must be provided to them. This paper describes a three-level conceptual view of provenance: the automatic capture of provenance in the semantic execution engine; the query and inference of provenance. The view adapts well to the three-phase procedure for automatic GIService composition and can increase understanding of the derivation history of geospatial data products. Provenance capture in the semantic execution engine fits well with the Semantic Web environment. Geospatial metadata is tracked during execution to augment provenance. A prototype system is implemented to illustrate the applicability of the approach. Numéro de notice : A2023-145 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi12030118 En ligne : https://doi.org/10.3390/ijgi12030118 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102848
in ISPRS International journal of geo-information > vol 12 n° 3 (March 2023) . - n° 118[article]SALT: A multifeature ensemble learning framework for mapping urban functional zones from VGI data and VHR images / Hao Wu in Computers, Environment and Urban Systems, vol 100 (March 2023)
[article]
Titre : SALT: A multifeature ensemble learning framework for mapping urban functional zones from VGI data and VHR images Type de document : Article/Communication Auteurs : Hao Wu, Auteur ; Wenting Luo, Auteur ; Anqi Lin, Auteur ; Fanghua Hao, Auteur ; Ana-Maria Olteanu-Raimond , Auteur ; Lanfa Liu, Auteur ; Yan Li, Auteur Année de publication : 2023 Projets : 1-Pas de projet / Article en page(s) : n° 101921 Note générale : Bibliographie
This work was supported by the National Natural Science Foundation of China [42201468, 42071358], Postdoctoral Innovation Talents Support Program of China [BX20220128], China Postdoctoral Science Foundation [2022M721283] and Fundamental Research Funds for the Central Universities [CCNU22QN018].Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] analyse multicritère
[Termes IGN] apprentissage automatique
[Termes IGN] boosting adapté
[Termes IGN] cartographie urbaine
[Termes IGN] Chine
[Termes IGN] détection du bâti
[Termes IGN] données localisées des bénévoles
[Termes IGN] image à très haute résolution
[Termes IGN] morphologie urbaine
[Termes IGN] OpenStreetMap
[Termes IGN] point d'intérêt
[Termes IGN] représentation spatiale
[Termes IGN] zone urbaineRésumé : (auteur) Urban functional zone mapping is essential for providing deeper insights into urban morphology and improving urban planning. The emergence of Volunteered Geographic Information (VGI), which provides abundant semantic data, offers a great opportunity to enrich land use information extracted from remote sensing (RS) images. Taking advantage of very-high-resolution (VHR) images and VGI data, this work proposed a SATL multifeature ensemble learning framework for mapping urban functional zones that integrated 65 features from the shapes of building objects, attributes of points of interest (POIs) tags, locations of cellphone users and textures of VHR images. The dimensionality of SALT features was reduced by the autoencoder, and the compressed features were applied to train the ensemble learning model composed of multiple classifiers for optimizing the urban functional zone classification. The effectiveness of the proposed framework was tested in an urbanized region of Nanchang City. The results indicated that the SALT features considering population dynamics and building shapes are comprehensive and feasible for urban functional zone mapping. The autoencoder has been proven efficient for dimension reduction of the original SALT features as it significantly improves the classification of urban functional zones. Moreover, the ensemble learning outperforms other machine learning models in terms of the accuracy and robustness when dealing with multi-classification tasks. Numéro de notice : A2023-125 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : GEOMATIQUE/IMAGERIE/INFORMATIQUE Nature : Article DOI : 10.1016/j.compenvurbsys.2022.101921 Date de publication en ligne : 06/12/2022 En ligne : https://doi.org/10.1016/j.compenvurbsys.2022.101921 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102504
in Computers, Environment and Urban Systems > vol 100 (March 2023) . - n° 101921[article]Siamese KPConv: 3D multiple change detection from raw point clouds using deep learning / Iris de Gelis in ISPRS Journal of photogrammetry and remote sensing, vol 197 (March 2023)
[article]
Titre : Siamese KPConv: 3D multiple change detection from raw point clouds using deep learning Type de document : Article/Communication Auteurs : Iris de Gelis, Auteur ; Sébastien Lefèvre, Auteur ; Thomas Corpetti, Auteur Année de publication : 2023 Article en page(s) : pp 274 - 291 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] apprentissage profond
[Termes IGN] bâtiment
[Termes IGN] détection de changement
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] modèle numérique de surface
[Termes IGN] réseau neuronal siamois
[Termes IGN] semis de points
[Termes IGN] végétation
[Termes IGN] zone urbaineRésumé : (auteur) This study is concerned with urban change detection and categorization in point clouds. In such situations, objects are mainly characterized by their vertical axis, and the use of native 3D data such as 3D Point Clouds (PCs) is, in general, preferred to rasterized versions because of significant loss of information implied by any rasterization process. Yet, for obvious practical reasons, most existing studies only focus on 2D images for change detection purpose. In this paper, we propose a method capable of performing change detection directly within 3D data. Despite recent deep learning developments in remote sensing, to the best of our knowledge there is no such method to tackle multi-class change segmentation that directly processes raw 3D PCs. Thereby, based on advances in deep learning for change detection in 2D images and for analysis of 3D point clouds, we propose a deep Siamese KPConv network that deals with raw 3D PCs to perform change detection and categorization in a single step. Experimental results are conducted on synthetic and real data of various kinds (LiDAR, multi-sensors). Tests performed on simulated low density LiDAR and multi-sensor datasets show that our proposed method can obtain up to 80% of mean of IoU over classes of changes, leading to an improvement ranging from 10% to 30% over the state-of-the-art. A similar range of improvements is attainable on real data. Then, we show that pre-training Siamese KPConv on simulated PCs allows us to greatly reduce (more than 3,000
) the annotations required on real data. This is a highly significant result to deal with practical scenarios. Finally, an adaptation of Siamese KPConv is realized to deal with change classification at PC scale. Our network overtakes the current state-of-the-art deep learning method by 23% and 15% of mean of IoU when assessed on synthetic and public Change3D datasets, respectively. The code is available at the following link: https://github.com/IdeGelis/torch-points3d-SiameseKPConv.Numéro de notice : A2023-147 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2023.02.001 Date de publication en ligne : 17/02/2023 En ligne : https://doi.org/10.1016/j.isprsjprs.2023.02.001 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102805
in ISPRS Journal of photogrammetry and remote sensing > vol 197 (March 2023) . - pp 274 - 291[article]A spatiotemporal data model and an index structure for computational time geography / Bi Yu Chen in International journal of geographical information science IJGIS, vol 37 n° 3 (March 2023)PermalinkThe potential of combining satellite and airborne remote sensing data for habitat classification and monitoring in forest landscapes / Anna Iglseder in International journal of applied Earth observation and geoinformation, vol 117 (March 2023)PermalinkWho owns the map? Data sovereignty and government spatial data collection, use, and dissemination / Peter A. Johnson in Transactions in GIS, vol 27 n° 1 (February 2023)PermalinkAnalysing urban growth using machine learning and open data: An artificial neural network modelled case study of five Greek cities / Pavlos Tsagkis in Sustainable Cities and Society, vol 89 (February 2023)PermalinkImproving quantitative structure models with an Huxley protocol based filter / Jan Hackenberg in Applied geomatics, vol 15 n° inconnu (2023)PermalinkLong-term changes in 3D urban form in four Spanish cities / Dario Domingo in Landscape and Urban Planning, vol 230 (February 2023)PermalinkStochastic multicriteria acceptability analysis as a forest management priority mapping approach based on airborne laser scanning and field inventory data / Parvez Rana in Landscape and Urban Planning, vol 230 (February 2023)PermalinkTopology-based individual tree segmentation for automated processing of terrestrial laser scanning point clouds / Xin Xu in International journal of applied Earth observation and geoinformation, vol 116 (February 2023)PermalinkGIS-based planning of buffer zones for protection of boreal streams and their riparian forests / Heikki Mykrä in Forest ecology and management, vol 528 (January-15 2023)Permalink3D building metrics for urban morphology / Anna Labetski in International journal of geographical information science IJGIS, vol 37 n° 1 (January 2023)Permalink