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Towards global scale segmentation with OpenStreetMap and remote sensing / Munazza Usmani in ISPRS Open Journal of Photogrammetry and Remote Sensing, vol 8 (April 2023)  

Public [article]  inISPRS Open Journal of Photogrammetry and Remote Sensing > vol 8 (April 2023) . - n° 100031 Titre : Towards global scale segmentation with OpenStreetMap and remote sensing Type de document : Article/Communication Auteurs : Munazza Usmani, Auteur ; Maurizio Napolitano, Auteur ; Francesca Bovolo, Auteur Année de publication : 2023 Article en page(s) : n° 100031 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] bâtiment [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] données localisées des bénévoles [Termes IGN] image à haute résolution [Termes IGN] information sémantique [Termes IGN] occupation du sol [Termes IGN] OpenStreetMap [Termes IGN] segmentation d'image [Termes IGN] segmentation sémantique [Termes IGN] utilisation du sol Résumé : (auteur) Land Use Land Cover (LULC) segmentation is a famous application of remote sensing in an urban environment. Up-to-date and complete data are of major importance in this field. Although with some success, pixel-based segmentation remains challenging because of class variability. Due to the increasing popularity of crowd-sourcing projects, like OpenStreetMap, the need for user-generated content has also increased, providing a new prospect for LULC segmentation. We propose a deep-learning approach to segment objects in high-resolution imagery by using semantic crowdsource information. Due to satellite imagery and crowdsource database complexity, deep learning frameworks perform a significant role. This integration reduces computation and labor costs. Our methods are based on a fully convolutional neural network (CNN) that has been adapted for multi-source data processing. We discuss the use of data augmentation techniques and improvements to the training pipeline. We applied semantic (U-Net) and instance segmentation (Mask R-CNN) methods and, Mask R–CNN showed a significantly higher segmentation accuracy from both qualitative and quantitative viewpoints. The conducted methods reach 91% and 96% overall accuracy in building segmentation and 90% in road segmentation, demonstrating OSM and remote sensing complementarity and potential for city sensing applications. Numéro de notice : A2023-148 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.ophoto.2023.100031 Date de publication en ligne : 16/02/2023 En ligne : https://doi.org/10.1016/j.ophoto.2023.100031 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102807 [article]

Semantic integration of OpenStreetMap and CityGML with formal concept analysis / Somayeh Ahmadian in Transactions in GIS, vol 26 n° 8 (December 2022)  

Public [article]  inTransactions in GIS > vol 26 n° 8 (December 2022) . - pp 3349 - 3373 Titre : Semantic integration of OpenStreetMap and CityGML with formal concept analysis Type de document : Article/Communication Auteurs : Somayeh Ahmadian, Auteur ; Parham Pahlavani, Auteur Année de publication : 2022 Article en page(s) : pp 3349 - 3373 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web [Termes IGN] analyse de groupement [Termes IGN] bâtiment [Termes IGN] CityGML [Termes IGN] classification par nuées dynamiques [Termes IGN] données localisées des bénévoles [Termes IGN] information sémantique [Termes IGN] ontologie [Termes IGN] OpenStreetMap [Termes IGN] qualité des données [Termes IGN] réseau sémantique Résumé : (auteur) Volunteered geographic information (VGI) provides geometric and descriptive sources of geospatial data. VGI exchange, reuse, and integration are serious challenges due to the subjective contribution process, lack of organization, and redundancy. This study aims to enhance the quality of VGI semantic data by presenting a new approach to integrating and formalizing the VGI semantic knowledge using formal concept analysis. The proposed approach is assessed using the building tags in OpenStreetMap (OSM) and CityGML. The alignment process discovers the conceptual overlap between the categories of Amenity (Others), Office, and Man-Made in Map Features (OSM) and Business and Trade, Recreation, Sport, and Industry in AbstractBuilding (CityGML). The k-means clustering of the results illustrated that class, usage/function, address, wheelchair, and website/wikidata/wikipedia are significant attributes to describe building categories. Moreover, results showed that the analysis of frequent itemsets and cluster characteristics provides significant information about custom tags in OSM's editing tools. Numéro de notice : A2022-909 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1111/tgis.13006 Date de publication en ligne : 02/12/2022 En ligne : https://doi.org/10.1111/tgis.13006 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102347 [article]

Improving deep learning on point cloud by maximizing mutual information across layers / Di Wang in Pattern recognition, vol 131 (November 2022)  

Public [article]  inPattern recognition > vol 131 (November 2022) . - n° 108892 Titre : Improving deep learning on point cloud by maximizing mutual information across layers Type de document : Article/Communication Auteurs : Di Wang, Auteur ; Lulu Tang, Auteur ; Xu Wang, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 108892 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage profond [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] détection d'objet [Termes IGN] entropie de Shannon [Termes IGN] information sémantique [Termes IGN] segmentation sémantique [Termes IGN] semis de points [Termes IGN] transformation géométrique [Termes IGN] vision par ordinateur [Termes IGN] visualisation 3D Résumé : (auteur) It is a fundamental and vital task to enhance the perception capability of the point cloud learning network in 3D machine vision applications. Most existing methods utilize feature fusion and geometric transformation to improve point cloud learning without paying enough attention to mining further intrinsic information across multiple network layers. Motivated to improve consistency between hierarchical features and strengthen the perception capability of the point cloud network, we propose exploring whether maximizing the mutual information (MI) across shallow and deep layers is beneficial to improve representation learning on point clouds. A novel design of Maximizing Mutual Information (MMI) Module is proposed, which assists the training process of the main network to capture discriminative features of the input point clouds. Specifically, the MMI-based loss function is employed to constrain the differences of semantic information in two hierarchical features extracted from the shallow and deep layers of the network. Extensive experiments show that our method is generally applicable to point cloud tasks, including classification, shape retrieval, indoor scene segmentation, 3D object detection, and completion, and illustrate the efficacy of our proposed method and its advantages over existing ones. Our source code is available at https://github.com/wendydidi/MMI.git. Numéro de notice : A2022-780 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : https://doi.org/10.1016/j.patcog.2022.108892 Date de publication en ligne : 08/07/2022 En ligne : https://doi.org/10.1016/j.patcog.2022.108892 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101859 [article]

A relation-augmented embedded graph attention network for remote sensing object detection / Shu Tian in IEEE Transactions on geoscience and remote sensing, vol 60 n° 10 (October 2022)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 60 n° 10 (October 2022) . - n° 1000718 Titre : A relation-augmented embedded graph attention network for remote sensing object detection Type de document : Article/Communication Auteurs : Shu Tian, Auteur ; Lihong Kang, Auteur ; Xiangwei Xing, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 1000718 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] détection d'objet [Termes IGN] graphe [Termes IGN] image à haute résolution [Termes IGN] information sémantique [Termes IGN] relation sémantique [Termes IGN] relation spatiale [Termes IGN] réseau neuronal de graphes [Termes IGN] SIFT (algorithme) Résumé : (auteur) Multiclass geospatial object detection in high spatial resolution remote sensing imagery (HSRI) is still a challenging task. The main reason is that the objects in HRSI are location-variable and semantic-confusable, which results in the difficulties in differentiating the complicated spatial patterns and deriving the implicitly semantic labels among different categories of objects. In this article, we propose a relation-augmented embedded graph attention network (EGAT), which enables the full exploitation of the underlying spatial and semantic relations among objects for improving the detection performance. Specifically, we first construct two sets of spatial and semantic graphs of objects–objects for object relations modeling. Second, a Siamese architecture-based embedding spatial and semantic graph attention network is designed for relations reasoning, which is implemented by introducing the long short-term memory (LSTM) mechanism into the EGAT, for learning the relations among different categories of intraobjects and interobjects. Driven by the spatial and semantic LSTM, the EGAT-LSTM can adaptively focus on the critical information of reason graphs for spatial–semantic correlation discrimination in the embedding non-Euclidean feature space. By this way, the EGAT-LSTM can effectively capture the global and local spatial–semantic relationships of objects–objects, and then produce relations-augmented features for improving the performance of object detection. We conduct comprehensive experiments on three public datasets for multiclass geospatial object detection. Our method achieves state-of-the-art performance, which demonstrates the superiority and effectiveness of the proposed method. Numéro de notice : A2022-766 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2021.3073269 Date de publication en ligne : 18/05/2021 En ligne : https://doi.org/10.1109/TGRS.2021.3073269 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101788 [article]

Deep learning method for Chinese multisource point of interest matching / Pengpeng Li in Computers, Environment and Urban Systems, vol 96 (September 2022)  

Public [article]  inComputers, Environment and Urban Systems > vol 96 (September 2022) . - n° 101821 Titre : Deep learning method for Chinese multisource point of interest matching Type de document : Article/Communication Auteurs : Pengpeng Li, Auteur ; Jiping Liu, Auteur ; An Luo, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 101821 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] appariement sémantique [Termes IGN] apprentissage profond [Termes IGN] classification par Perceptron multicouche [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] extraction de traits caractéristiques [Termes IGN] inférence sémantique [Termes IGN] information sémantique [Termes IGN] point d'intérêt [Termes IGN] représentation vectorielle [Termes IGN] traitement du langage naturel Résumé : (auteur) Multisource point of interest (POI) matching refers to the pairing of POIs that refer to the same geographic entity in different data sources. This also constitutes the core issue in geospatial data fusion and update. The existing methods cannot effectively capture the complex semantic information from a text, and the manually defined rules largely affect matching results. This study developed a multisource POI matching method based on deep learning that transforms the POI pair matching problem into a binary classification problem. First, we used three different Chinese word segmentation methods to segment the POI text attributes and used the segmentation results to train the Word2Vec model to generate the corresponding word vector representation. Then, we used the text convolutional neural network (Text-CNN) and multilayer perceptron (MLP) to extract the POI attributes' features and generate the corresponding feature vector representation. Finally, we used the enhanced sequential inference model (ESIM) to perform local inference and inference combination on each attribute to realize the classification of POI pairs. We used the POI dataset containing Baidu Map, Tencent Map, and Gaode Map from Chengdu to train, verify, and test the model. The experimental results show that the matching precision, recall rate, and F1 score of the proposed method exceed 98% on the test set, and it is significantly better than the existing matching methods. Numéro de notice : A2022-513 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Article DOI : 10.1016/j.compenvurbsys.2022.101821 Date de publication en ligne : 18/06/2022 En ligne : https://doi.org/10.1016/j.compenvurbsys.2022.101821 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101053 [article]

Point-of-interest detection from Weibo data for map updating / Xue Yang in Transactions in GIS, vol 26 n° 6 (September 2022)  

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Change detection in street environments based on mobile laser scanning: A fuzzy spatial reasoning approach / Joachim Gehrung in ISPRS Open Journal of Photogrammetry and Remote Sensing, vol 5 (August 2022)  

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Summarizing large scale 3D mesh for urban navigation / Imeen Ben Salah in Robotics and autonomous systems, vol 152 (June 2022)  

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Mining crowdsourced trajectory and geo-tagged data for spatial-semantic road map construction / Jincai Huang in Transactions in GIS, vol 26 n° 2 (April 2022)  

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A method of vision aided GNSS positioning using semantic information in complex urban environment / Rui Zhai in Remote sensing, vol 14 n° 4 (February-2 2022)  

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Quickly locating POIs in large datasets from descriptions based on improved address matching and compact qualitative representations / Ruozhen Cheng in Transactions in GIS, vol 26 n° 1 (February 2022)  

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Annotation sémantique pour la géolocalisation d'entités spatiales dans des tweets / Gaëtan Caillaut (2022)  

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Representing vector geographic information as a tensor for deep learning based map generalisation / Azelle Courtial (2022)  

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Robustness of visual SLAM techniques to light changing conditions / Xi Wang (2022)  

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Traitement possibiliste d'images, application au recalage d'images / Wissal Ben Markouza (2022)  

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