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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)
[article]
Titre : A spatiotemporal data model and an index structure for computational time geography Type de document : Article/Communication Auteurs : Bi Yu Chen, Auteur ; Yu-Bo Luo, Auteur ; Tao Jia, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : pp 550 - 583 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] approche hiérarchique
[Termes IGN] données massives
[Termes IGN] données spatiotemporelles
[Termes IGN] modèle conceptuel de données spatio-temporelles
[Termes IGN] requête spatiotemporelle
[Termes IGN] stockage de données
[Termes IGN] Time-geographyRésumé : (auteur) The availability of Spatiotemporal Big Data has provided a golden opportunity for time geographical studies that have long been constrained by the lack of individual-level data. However, how to store, manage, and query a huge number of time geographic entities effectively and efficiently with complex spatiotemporal characteristics and relationships poses a significant challenge to contemporary GIS platforms. In this article, a hierarchical compressed linear reference (CLR) model is proposed to transform network-constrained time geographic entities from three-dimensional (3D) (x, y, t) space into two-dimensional (2D) space. Accordingly, time geographic entities can be represented as 2D spatial entities and stored in a classical spatial database. The proposed CLR model supports a hierarchical linear reference system (LRS) including not only underlying a link-based LRS but also multiple higher-level route-based LRSs. In addition, an LRS-based spatiotemporal index structure is developed to index both time geographic entities and the corresponding hierarchical network. The results of computational experiments on large datasets of space–time paths and prisms show that the proposed hierarchical CLR model is effective at storing and managing time geographic entities in road networks. The developed index structure achieves satisfactory query performance in milliseconds on large datasets of time geographic entities. Numéro de notice : A2023-153 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2022.2128192 Date de publication en ligne : 03/10/2023 En ligne : https://doi.org/10.1080/13658816.2022.2128192 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102836
in International journal of geographical information science IJGIS > vol 37 n° 3 (March 2023) . - pp 550 - 583[article]A unified attention paradigm for hyperspectral image classification / Qian Liu in IEEE Transactions on geoscience and remote sensing, vol 61 n° 3 (March 2023)
[article]
Titre : A unified attention paradigm for hyperspectral image classification Type de document : Article/Communication Auteurs : Qian Liu, Auteur ; Zebin Wu, Auteur ; Yang Xu, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : n° 5506316 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] attention (apprentissage automatique)
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] image hyperspectrale
[Termes IGN] précision de la classification
[Termes IGN] séparateur à vaste margeRésumé : (auteur) Attention mechanisms improve the classification accuracies by enhancing the salient information for hyperspectral images (HSIs). However, existing HSI attention models are driven by advanced achievements of computer vision, which are not able to fully exploit the spectral–spatial structure prior of HSIs and effectively refine features from a global perspective. In this article, we propose a unified attention paradigm (UAP) that defines the attention mechanism as a general three-stage process including optimizing feature representations, strengthening information interaction, and emphasizing meaningful information. Meanwhile, we designed a novel efficient spectral–spatial attention module (ESSAM) under this paradigm, which adaptively adjusts feature responses along the spectral and spatial dimensions at an extremely low parameter cost. Specifically, we construct a parameter-free spectral attention block that employs multiscale structured encodings and similarity calculations to perform global cross-channel interactions, and a memory-enhanced spatial attention block that captures key semantics of images stored in a learnable memory unit and models global spatial relationship by constructing semantic-to-pixel dependencies. ESSAM takes full account of the spatial distribution and low-dimensional characteristics of HSIs, with better interpretability and lower complexity. We develop a dense convolutional network based on efficient spectral–spatial attention network (ESSAN) and experiment on three real hyperspectral datasets. The experimental results demonstrate that the proposed ESSAM brings higher accuracy improvement compared to advanced attention models. Numéro de notice : A2023-185 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2023.3257321 Date de publication en ligne : 15/12/2023 En ligne : https://doi.org/10.1109/TGRS.2023.3257321 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102957
in IEEE Transactions on geoscience and remote sensing > vol 61 n° 3 (March 2023) . - n° 5506316[article]Validation of Island 3D-mapping based on UAV spatial point cloud optimization: a case study in Dongluo Island of China / Jian Wu in Photogrammetric Engineering & Remote Sensing, PERS, vol 89 n° 3 (March 2023)
[article]
Titre : Validation of Island 3D-mapping based on UAV spatial point cloud optimization: a case study in Dongluo Island of China Type de document : Article/Communication Auteurs : Jian Wu, Auteur ; Shifeng Fu, Auteur ; Peng Chen, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : pp 173 - 182 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] cartographie 3D
[Termes IGN] Chine
[Termes IGN] île
[Termes IGN] image captée par drone
[Termes IGN] modélisation 3D
[Termes IGN] semis de points
[Termes IGN] télédétection aérienneRésumé : (Auteur) The unmanned aerial vehicle (UAV) remote sensing is of small volume, low cost, fine timeliness, and high spatial resolution, and has the special advantage on island surveying. Focus on the inaccurate elevation of non-ground point cloud without lidar device, this study explored a methodology for island three-dimensional (3D) mapping and modelling based on spatial point clouds optimization with a K-Nearest Neighbors Adaptive Inverse Distance Weighted (K-AIDW) interpolation algorithm. By classifying the UAV point clouds into ground, vegatetation, and structure, the K-AIDW algorithm was applied to optimize the elevations of non-ground point clouds (vegetation and structure) to recalculate Z values. The aerophotogrammetry result was generated based on the optimized spatial point clouds. Finally, the 3D model of Dongluo Island was reconstructed and rendered in Metashape. The accuracy evaluation result shows that the max-errors of ground control points (–0.0154 in X, 0.0305 in Y, and 0.0133 in Z) and the checkpoints (–0.091 in X, –0.176 in Y, and 0.338 in Z) can meet the error-tolerance requirements of the corresponding terrain on the 1:500 scale set by the national standard of GB/T 23236-2009 in China. It is found that the K-AIDW algorithm displayed the best Z accuracy (root-mean-square error of 0.2538) compared with IDW (0.3668) and no-optimized (1.6012), proving it is an effective methodology for improving 3D-modelling accuracy of island. Numéro de notice : A2023-172 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.22-00109R2 Date de publication en ligne : 01/03/2023 En ligne : https://doi.org/10.14358/PERS.22-00109R2 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102923
in Photogrammetric Engineering & Remote Sensing, PERS > vol 89 n° 3 (March 2023) . - pp 173 - 182[article]Réservation
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