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Estimating urban functional distributions with semantics preserved POI embedding / Weiming Huang in International journal of geographical information science IJGIS, vol 36 n° 10 (October 2022)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 36 n° 10 (October 2022) . - pp 1905 - 1930 Titre : Estimating urban functional distributions with semantics preserved POI embedding Type de document : Article/Communication Auteurs : Weiming Huang, Auteur ; Lizhen Cui, Auteur ; Meng Chen, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 1905 - 1930 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] attention (apprentissage automatique) [Termes IGN] Chine [Termes IGN] classe sémantique [Termes IGN] classification par réseau neuronal récurrent [Termes IGN] distribution spatiale [Termes IGN] échantillonnage [Termes IGN] lissage de données [Termes IGN] matrice de co-occurrence [Termes IGN] Perceptron multicouche [Termes IGN] point d'intérêt [Termes IGN] triangulation de Delaunay [Termes IGN] zone urbaine Résumé : (auteur) We present a novel approach for estimating the proportional distributions of function types (i.e. functional distributions) in an urban area through learning semantics preserved embeddings of points-of-interest (POIs). Specifically, we represent POIs as low-dimensional vectors to capture (1) the spatial co-occurrence patterns of POIs and (2) the semantics conveyed by the POI hierarchical categories (i.e. categorical semantics). The proposed approach utilizes spatially explicit random walks in a POI network to learn spatial co-occurrence patterns, and a manifold learning algorithm to capture categorical semantics. The learned POI vector embeddings are then aggregated to generate regional embeddings with long short-term memory (LSTM) and attention mechanisms, to take account of the different levels of importance among the POIs in a region. Finally, a multilayer perceptron (MLP) maps regional embeddings to functional distributions. A case study in Xiamen Island, China implements and evaluates the proposed approach. The results indicate that our approach outperforms several competitive baseline models in all evaluation measures, and yields a relatively high consistency between the estimation and ground truth. In addition, a comprehensive error analysis unveils several intrinsic limitations of POI data for this task, e.g. ambiguous linkage between POIs and functions. Numéro de notice : A2022-738 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/13658816.2022.2040510 Date de publication en ligne : 08/03/2022 En ligne : https://doi.org/10.1080/13658816.2022.2040510 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101714 [article]

Street-view imagery guided street furniture inventory from mobile laser scanning point clouds / Yuzhou Zhou in ISPRS Journal of photogrammetry and remote sensing, vol 189 (July 2022)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 189 (July 2022) . - pp 63 - 77 Titre : Street-view imagery guided street furniture inventory from mobile laser scanning point clouds Type de document : Article/Communication Auteurs : Yuzhou Zhou, Auteur ; Xu Han, Auteur ; Mingjun Peng, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 63 - 77 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] détection d'objet [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] image Streetview [Termes IGN] instance [Termes IGN] inventaire [Termes IGN] jeu de données localisées [Termes IGN] masque [Termes IGN] mobilier urbain [Termes IGN] segmentation sémantique [Termes IGN] semis de points [Termes IGN] séparateur à vaste marge [Termes IGN] Shanghai (Chine) [Termes IGN] Wuhan (Chine) Résumé : (auteur) Outdated or sketchy inventory of street furniture may misguide the planners on the renovation and upgrade of transportation infrastructures, thus posing potential threats to traffic safety. Previous studies have taken their steps using point clouds or street-view imagery (SVI) for street furniture inventory, but there remains a gap to balance semantic richness, localization accuracy and working efficiency. Therefore, this paper proposes an effective pipeline that combines SVI and point clouds for the inventory of street furniture. The proposed pipeline encompasses three steps: (1) Off-the-shelf street furniture detection models are applied on SVI for generating two-dimensional (2D) proposals and then three-dimensional (3D) point cloud frustums are accordingly cropped; (2) The instance mask and the instance 3D bounding box are predicted for each frustum using a multi-task neural network; (3) Frustums from adjacent perspectives are associated and fused via multi-object tracking, after which the object-centric instance segmentation outputs the final street furniture with 3D locations and semantic labels. This pipeline was validated on datasets collected in Shanghai and Wuhan, producing component-level street furniture inventory of nine classes. The instance-level mean recall and precision reach 86.4%, 80.9% and 83.2%, 87.8% respectively in Shanghai and Wuhan, and the point-level mean recall, precision, weighted coverage all exceed 73.7%. Numéro de notice : A2022-403 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.isprsjprs.2022.04.023 Date de publication en ligne : 12/05/2022 En ligne : https://doi.org/10.1016/j.isprsjprs.2022.04.023 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100711 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2022071	SL	Revue	Centre de documentation	Revues en salle	Disponible

Semantic segmentation of high-resolution remote sensing images based on a class feature attention mechanism fused with Deeplabv3+ / Zhimin Wang in Computers & geosciences, vol 158 (January 2022)  

Public [article]  inComputers & geosciences > vol 158 (January 2022) . - n° 104969 Titre : Semantic segmentation of high-resolution remote sensing images based on a class feature attention mechanism fused with Deeplabv3+ Type de document : Article/Communication Auteurs : Zhimin Wang, Auteur ; Jiasheng Wang, Auteur ; Kun Yang, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 104969 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage profond [Termes IGN] attention (apprentissage automatique) [Termes IGN] classe sémantique [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] image à haute résolution [Termes IGN] image Gaofen [Termes IGN] raisonnement sémantique [Termes IGN] segmentation sémantique Résumé : (auteur) Aiming at solving the problems of inaccurate segmentation of edge targets, inconsistent segmentation of different types of targets, and slow prediction efficiency on semantic segmentation of high-resolution remote sensing images by classical semantic segmentation network, this study proposed a class feature attention mechanism fused with an improved Deeplabv3+ network called CFAMNet for semantic segmentation of common features in remote sensing images. First, the correlation between classes is enhanced using the class feature attention module to extract and process different categories of semantic information better. Second, the multi-parallel atrous spatial pyramid pooling structure is used to enhance the correlation between spaces, to extract the context information of different scales of an image better. Finally, the encoder-decoder structure is used to refine the segmentation results. The segmentation effect of the proposed network is verified by experiments on the public data set GaoFen image dataset (GID). The experimental results show that the CFAMNet can achieve the mean intersection over union (MIOU) and overall accuracy (OA) of 77.22% and 85.01%, respectively, on the GID, thus surpassing the current mainstream semantic segmentation networks. Numéro de notice : A2022-030 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.cageo.2021.104969 Date de publication en ligne : 26/10/2021 En ligne : https://doi.org/10.1016/j.cageo.2021.104969 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99269 [article]

Adaptive edge preserving maps in Markov random fields for hyperspectral image classification / Chao Pan in IEEE Transactions on geoscience and remote sensing, vol 59 n° 10 (October 2021)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 59 n° 10 (October 2021) . - pp 8568 - 8583 Titre : Adaptive edge preserving maps in Markov random fields for hyperspectral image classification Type de document : Article/Communication Auteurs : Chao Pan, Auteur ; Xiuping Jia, Auteur ; Jie Li, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 8568 - 8583 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] accentuation de contours [Termes IGN] algorithme Graph-Cut [Termes IGN] champ aléatoire de Markov [Termes IGN] classe d'objets [Termes IGN] détection de contours [Termes IGN] étiquette de classe [Termes IGN] image AVIRIS [Termes IGN] image hyperspectrale [Termes IGN] optimisation (mathématiques) [Termes IGN] segmentation d'image Résumé : (auteur) This article presents a novel adaptive edge preserving (aEP) scheme in Markov random fields (MRFs) for hyperspectral image (HSI) classification. MRF regularization usually suffered from over-smoothing at boundaries and insufficient refinement within class objects. This work divides and conquers this problem class-by-class, and integrates K ( K−1 )/2 ( K is the class number) aEP maps (aEPMs) in MRF model. Spatial label dependence measure (SLDM) is designed to estimate the interpixel label dependence for given spectral similarity measure. For each class pair, aEPM is optimized by maximizing the difference between intraclass and interclass SLDM. Then, aEPMs are integrated with multilevel logistic (MLL) model to regularize the raw pixelwise labeling obtained by spectral and spectral–spatial methods, respectively. The graph-cuts-based α β -swap algorithm is modified to optimize the designed energy function. Moreover, to evaluate the final refined results at edges and small details thoroughly, segmentation evaluation metrics are introduced. Experiments conducted on real HSI data denote the superiority of aEPMs in evaluation metrics and region consistency, especially in detail preservation. Numéro de notice : A2021-713 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.3035642 Date de publication en ligne : 16/11/2020 En ligne : https://doi.org/10.1109/TGRS.2020.3035642 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98618 [article]

A novel class-specific object-based method for urban change detection using high-resolution remote sensing imagery / Ting Bai in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 4 (April 2021)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 87 n° 4 (April 2021) . - pp 249-262 Titre : A novel class-specific object-based method for urban change detection using high-resolution remote sensing imagery Type de document : Article/Communication Auteurs : Ting Bai, Auteur ; Kaimin Sun, Auteur ; Wenzhuo Li, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 249-262 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] changement d'occupation du sol [Termes IGN] classe d'objets [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] détection de changement [Termes IGN] détection du bâti [Termes IGN] image à haute résolution [Termes IGN] milieu urbain [Termes IGN] segmentation multi-échelle Résumé : (Auteur) A single-scale object-based change-detection classifier can distinguish only global changes in land cover, not the more granular and local changes in urban areas. To overcome this issue, a novel class-specific object-based change-detection method is proposed. This method includes three steps: class-specific scale selection, class-specific classifier selection, and land cover change detection. The first step combines multi-resolution segmentation and a random forest to select the optimal scale for each change type in land cover. The second step links multi-scale hierarchical sampling with a classifier such as random forest, support vector machine, gradient-boosting decision tree, or Adaboost; the algorithm automatically selects the optimal classifier for each change type in land cover. The final step employs the optimal classifier to detect binary changes and from-to changes for each change type in land cover. To validate the proposed method, we applied it to two high-resolution data sets in urban areas and compared the change-detection results of our proposed method with that of principal component analysis k-means, object-based change vector analysis, and support vector machine. The experimental results show that our proposed method is more accurate than the other methods. The proposed method can address the high levels of complexity found in urban areas, although it requires historical land cover maps as auxiliary data. Numéro de notice : A2021-332 Affiliation des auteurs : non IGN Thématique : IMAGERIE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.87.4.249 Date de publication en ligne : 01/04/2021 En ligne : https://doi.org/10.14358/PERS.87.4.249 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97528 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
105-2021041	SL	Revue	Centre de documentation	Revues en salle	Disponible

SemCity 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)  

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Exploring semantic elements for urban scene recognition: Deep integration of high-resolution imagery and OpenStreetMap (OSM) / Wenzhi Zhao in ISPRS Journal of photogrammetry and remote sensing, vol 151 (May 2019)  

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Towards visual urban scene understanding for autonomous vehicle path tracking using GPS positioning data / Citlalli Gamez Serna (2019)  

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Label propagation with ensemble of pairwise geometric relations : towards robust large-scale retrieval of object instances / Xiaomeng Wu in International journal of computer vision, vol 126 n° 7 (July 2018)  

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Labelling hierarchy for street maps using centrality measures / Wasim Shoman in Cartographic journal (the), vol 55 n° 1 (February 2018)  

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A hydrological sensor web ontology based on the SSN ontology: A case study for a flood / Chao Wang in ISPRS International journal of geo-information, vol 7 n° 1 (January 2018)  

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Geometric features and their relevance for 3D point cloud classification / Martin Weinmann in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol IV-1/W1 (May 2017)  

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Integrating social network data into GISystems / Clio Andris in International journal of geographical information science IJGIS, vol 30 n° 9-10 (September - October 2016)  

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A survey on web data linking / Manel Achichi in Ingénierie des systèmes d'information, ISI : Revue des sciences et technologies de l'information, RSTI, vol 21 n° 5 - 6 (septembre - décembre 2016)  

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Carte de Kohonen et classification ascendante hiérarchique pour l’analyse de données géohistoriques / Ana-Maria Olteanu-Raimond in Revue internationale de géomatique, vol 25 n° 4 (octobre - décembre 2015)  

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