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Recognition of building group patterns using graph convolutional network / Rong Zhao in Cartography and Geographic Information Science, Vol 47 n° 5 (September 2020)  

Public [article]  inCartography and Geographic Information Science > Vol 47 n° 5 (September 2020) . - pp 400 - 417 Titre : Recognition of building group patterns using graph convolutional network Type de document : Article/Communication Auteurs : Rong Zhao, Auteur ; Tinghua Ai, Auteur ; Wenhao Yu, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 400 - 417 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes descripteurs IGN] classification par réseau neuronal convolutif [Termes descripteurs IGN] données topographiques [Termes descripteurs IGN] espace urbain [Termes descripteurs IGN] généralisation du bâti [Termes descripteurs IGN] graphe [Termes descripteurs IGN] modélisation du bâti [Termes descripteurs IGN] reconnaissance de formes Résumé : (auteur) Recognition of building group patterns is of great significance for understanding and modeling the urban space. However, many current methods cannot fully utilize spatial information and have trouble efficiently dealing with topographic data with high complexity. The design of intelligent computational models that can act directly on topographic data to extract spatial features is critical. To this end, we propose a novel deep neural network based on graph convolutions to automatically identify building group patterns with arbitrary forms. The method first models buildings by a general graph, and then the neural network simultaneously learns the structural information as well as vertex attributes to classify building objects. We apply this method to real building data, and the experimental results show that the proposed method can effectively capture spatial information to make more accurate predictions than traditional methods. Numéro de notice : A2020-510 Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/15230406.2020.1757512 date de publication en ligne : 12/06/2020 En ligne : https://doi.org/10.1080/15230406.2020.1757512 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95663 [article]

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Traffic signal detection from in-vehicle GPS speed profiles using functional data analysis and machine learning / Yann Méneroux in International Journal of Data Science and Analytics JDSA, vol 10 n° 1 (June 2020)  

Public [article]  inInternational Journal of Data Science and Analytics JDSA > vol 10 n° 1 (June 2020) . - pp 101 - 119 Titre : Traffic signal detection from in-vehicle GPS speed profiles using functional data analysis and machine learning Type de document : Article/Communication Auteurs : Yann Méneroux , Auteur ; Arnaud Le Guilcher , Auteur ; Guillaume Saint Pierre, Auteur ; Mohammad Ghasemi Hamed, Auteur ; Sébastien Mustière , Auteur ; Olivier Orfila, Auteur Année de publication : 2020 Projets : 2-Pas d'info accessible - article non ouvert / Article en page(s) : pp 101 - 119 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes descripteurs IGN] analyse fonctionnelle (mathématiques) [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] carte routière [Termes descripteurs IGN] classification par forêts aléatoires [Termes descripteurs IGN] contenu généré par les utilisateurs [Termes descripteurs IGN] détection d'objet [Termes descripteurs IGN] données routières [Termes descripteurs IGN] feu de circulation [Termes descripteurs IGN] inférence [Termes descripteurs IGN] reconnaissance de formes [Termes descripteurs IGN] signalisation routière [Termes descripteurs IGN] trace GPS [Termes descripteurs IGN] trafic routier [Termes descripteurs IGN] transformation en ondelettes [Termes descripteurs IGN] vitesse Résumé : (auteur) The increasing availability of large-scale global positioning system data stemming from in-vehicle-embedded terminal devices enables the design of methods deriving road network cartographic information from drivers’ recorded traces. Some machine learning approaches have been proposed in the past to train automatic road network map inference, and recently this approach has been successfully extended to infer road attributes as well, such as speed limitation or number of lanes. In this paper, we address the problem of detecting traffic signals from a set of vehicle speed profiles, under a classification perspective. Each data instance is a speed versus distance plot depicting over a hundred profiles on a 100-m-long road span. We proposed three different ways of deriving features: The first one relies on the raw speed measurements; the second one uses image recognition techniques; and the third one is based on functional data analysis. We input them into most commonly used classification algorithms, and a comparative analysis demonstrated that a functional description of speed profiles with wavelet transforms seems to outperform the other approaches with most of the tested classifiers. It also highlighted that random forests yield an accurate detection of traffic signals, regardless of the chosen feature extraction method, while keeping a remarkably low confusion rate with stop signs. Numéro de notice : A2019-497 Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s41060-019-00197-x date de publication en ligne : 04/10/2019 En ligne : https://doi.org/10.1007/s41060-019-00197-x Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93755 [article]

Deep learning for enrichment of vector spatial databases: Application to highway interchange / Guillaume Touya in ACM Transactions on spatial algorithms and systems, vol 6 n° 3 (May 2020)    

Public [article]  inACM Transactions on spatial algorithms and systems > vol 6 n° 3 (May 2020) . - 21 p. Titre : Deep learning for enrichment of vector spatial databases: Application to highway interchange Type de document : Article/Communication Auteurs : Guillaume Touya , Auteur ; Imran Lokhat , Auteur Année de publication : 2020 Projets : 1-Pas de projet / Article en page(s) : 21 p. Note générale : bibliographie version preprint sur HAL https://hal.archives-ouvertes.fr/hal-02465244 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] base d'apprentissage [Termes descripteurs IGN] base de données vectorielles [Termes descripteurs IGN] classification par réseau neuronal convolutif [Termes descripteurs IGN] échangeur routier [Termes descripteurs IGN] enrichissement sémantique [Termes descripteurs IGN] reconnaissance d'objets [Termes descripteurs IGN] segmentation d'image Résumé : (auteur) Spatial analysis and pattern recognition with vector spatial data is particularly useful to enrich raw data. In road networks, for instance, there are many patterns and structures that are implicit with only road line features, among which highway interchange appeared very complex to recognize with vector-based techniques. The goal is to find the roads that belong to an interchange, such as the slip roads and the highway roads connected to the slip roads. To go further than state-of-the-art vector-based techniques, this article proposes to use raster-based deep learning techniques to recognize highway interchanges. The contribution of this work is to study how to optimally convert vector data into small images suitable for state-of-the-art deep learning models. Image classification with a convolutional neural network (i.e., is there an interchange in this image or not?) and image segmentation with a u-net (i.e., find the pixels that cover the interchange) are experimented and give better results than existing vector-based techniques in this specific use case (99.5% against 74%). Numéro de notice : A2020-365 Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1145/3382080 date de publication en ligne : 01/04/2020 En ligne : https://doi.org/10.1145/3382080 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95399 [article]

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Recognizing linear building patterns in topographic data by using two new indices based on Delaunay triangulation / Xianjin He in ISPRS International journal of geo-information, vol 9 n° 4 (April 2020)  

Public [article]  inISPRS International journal of geo-information > vol 9 n° 4 (April 2020) . - 14 p. Titre : Recognizing linear building patterns in topographic data by using two new indices based on Delaunay triangulation Type de document : Article/Communication Auteurs : Xianjin He, Auteur ; Min Deng, Auteur Année de publication : 2020 Article en page(s) : 14 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes descripteurs IGN] adjacence [Termes descripteurs IGN] direction [Termes descripteurs IGN] discontinuité [Termes descripteurs IGN] données topographiques [Termes descripteurs IGN] modèle linéaire [Termes descripteurs IGN] modèle numérique du bâti [Termes descripteurs IGN] reconnaissance de formes [Termes descripteurs IGN] segmentation [Termes descripteurs IGN] triangulation de Delaunay [Termes descripteurs IGN] voisinage (topologie) Résumé : (auteur) Building pattern recognition is fundamental to a wide range of downstream applications, such as urban landscape evaluation, social analyses, and map generalization. Although many studies have been conducted, there is still a lack of satisfactory results, due to the imprecision of the relative direction model of any two adjacent buildings and the ineffective extraction methods. This study aims to provide an alternative for quantifying the direction and the spatial continuity of any two buildings on the basis of the Delaunay triangulation for the recognition of linear building patterns. First, constrained Delaunay triangulations (CDTs) are created for all buildings within each block and every two adjacent buildings. Then, the spatial continuity index (SCI), the direction index (DI), and other spatial relations (e.g., distance) of every two adjacent buildings are derived using the CDT. Finally, the building block is modelled as a graph based on derived matrices, and a graph segmentation approach is proposed to extract linear building patterns. In the segmentation process, the edges of the graph are removed first, according to the global thresholds of the SCI and distance, and are subsequently subdivided into subgraphs on direction rules. The proposed method is tested using three datasets. The experimental results suggest that the proposed method can recognize both collinear and curvilinear building patterns, given that the correctness values are all above 92% for the three study areas. The results also demonstrate that the novel SCI can effectively filter many insignificant neighbor relationships in the graph segmentation process. It is noteworthy that the proposed DI is capable of measuring building relative directions accurately and works efficiently in linear building pattern extraction. Numéro de notice : A2020-267 Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi9040231 date de publication en ligne : 09/04/2020 En ligne : https://doi.org/10.3390/ijgi9040231 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95031 [article]

Classification and segmentation of mining area objects in large-scale spares Lidar point cloud using a novel rotated density network / Yueguan Yan in ISPRS International journal of geo-information, vol 9 n° 3 (March 2020)  

Public [article]  inISPRS International journal of geo-information > vol 9 n° 3 (March 2020) . - 19 p. Titre : Classification and segmentation of mining area objects in large-scale spares Lidar point cloud using a novel rotated density network Type de document : Article/Communication Auteurs : Yueguan Yan, Auteur ; Haixu Yan, Auteur ; Junting Guo, Auteur ; Huayang Dai, Auteur Année de publication : 2020 Article en page(s) : 19 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] classification barycentrique [Termes descripteurs IGN] classification orientée objet [Termes descripteurs IGN] classification par réseau neuronal convolutif [Termes descripteurs IGN] corrélation automatique de points homologues [Termes descripteurs IGN] densité des points [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] objet 3D [Termes descripteurs IGN] reconnaissance d'objets [Termes descripteurs IGN] semis de points clairsemés Résumé : (auteur) The classification and segmentation of large-scale, sparse, LiDAR point cloud with deep learning are widely used in engineering survey and geoscience. The loose structure and the non-uniform point density are the two major constraints to utilize the sparse point cloud. This paper proposes a lightweight auxiliary network, called the rotated density-based network (RD-Net), and a novel point cloud preprocessing method, Grid Trajectory Box (GT-Box), to solve these problems. The combination of RD-Net and PointNet was used to achieve high-precision 3D classification and segmentation of the sparse point cloud. It emphasizes the importance of the density feature of LiDAR points for 3D object recognition of sparse point cloud. Furthermore, RD-Net plus PointCNN, PointNet, PointCNN, and RD-Net were introduced as comparisons. Public datasets were used to evaluate the performance of the proposed method. The results showed that the RD-Net could significantly improve the performance of sparse point cloud recognition for the coordinate-based network and could improve the classification accuracy to 94% and the segmentation per-accuracy to 70%. Additionally, the results concluded that point-density information has an independent spatial–local correlation and plays an essential role in the process of sparse point cloud recognition. Numéro de notice : A2020-256 Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 0.3390/ijgi9030182 date de publication en ligne : 24/03/2020 En ligne : https://doi.org/10.3390/ijgi9030182 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95012 [article]

Learning sequential slice representation with an attention-embedding network for 3D shape recognition and retrieval in MLS point clouds / Zhipeng Luo in ISPRS Journal of photogrammetry and remote sensing, vol 161 (March 2020)  

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Georeferenced measurements of building objects with their simultaneous shape detection / Edward Osada in Survey review, Vol 52 n°370 (January 2020)  

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Validating the correct wearing of protection mask by taking a selfie: design of a mobile application "CheckYourMask" to limit the spread of COVID-19 / Karim Hammoudi (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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Learning high-level features by fusing multi-view representation of MLS point clouds for 3D object recognition in road environments / Zhipeng Luo in ISPRS Journal of photogrammetry and remote sensing, vol 150 (April 2019)  

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Learning to segment moving objects / Pavel Tokmakov in International journal of computer vision, vol 127 n° 3 (March 2019)  

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Generating urban morphologies at large scales / Juste Raimbault (2019)    

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Spatially sensitive statistical shape analysis for pedestrian recognition from LIDAR data / Michalis A. Savelonas in Computer Vision and image understanding, vol 171 (June 2018)  

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Do semantic parts emerge in convolutional neural networks? / Abel Gonzalez-Garcia in International journal of computer vision, vol 126 n° 5 (May 2018)  

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Fine-grained object recognition and zero-shot learning in remote sensing imagery / Gencer Sumbul in IEEE Transactions on geoscience and remote sensing, vol 56 n° 2 (February 2018)  

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