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

Public Titre : Generating urban morphologies at large scales Type de document : Article/Communication Auteurs : Juste Raimbault, Auteur ; Julien Perret , Auteur Congrès : Alife 2019, Conference on Artificial life (29 juillet - 2 aout 2019; Newcastle, Royaume-uni) , Commanditaire Editeur : Cambridge [Massachusetts - Etats-Unis] : MIT Press Année de publication : 2019 Projets : DynamiCity / Importance : 8 p. Format : 21 x 30 cm Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes descripteurs IGN] Europe (géographie politique) [Termes descripteurs IGN] grande échelle [Termes descripteurs IGN] indicateur [Termes descripteurs IGN] modèle numérique [Termes descripteurs IGN] modélisation de processus [Termes descripteurs IGN] morphologie mathématique [Termes descripteurs IGN] morphologie urbaine [Termes descripteurs IGN] OpenStreetMap [Termes descripteurs IGN] reconnaissance de formes [Termes descripteurs IGN] zone urbaine Résumé : (Auteur) At large scales, typologies of urban form and corresponding generating processes remain an open question with important implications regarding urban planning policies and sustainability. We propose in this paper to generate urban configurations at large scales, typically of districts, with morphogene-sis models, and compare these to real configurations according to morphological indicators. Real values are computed on a large sample of districts taken in European urban areas. We calibrate each model and show their complementarity to approach the variety of real urban configurations, paving the way to multi-model approaches of urban morphogenesis. Numéro de notice : C2019-013 Thématique : GEOMATIQUE Nature : Communication nature-HAL : ComSansActesPubliés-Unpublished DOI : sans En ligne : https://halshs.archives-ouvertes.fr/halshs-02265415 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93262

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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)  

Public [article]  inComputer Vision and image understanding > vol 171 (June 2018) . - pp 1 - 9 Titre : Spatially sensitive statistical shape analysis for pedestrian recognition from LIDAR data Type de document : Article/Communication Auteurs : Michalis A. Savelonas, Auteur ; Ioannis Pratikakis, Auteur ; Theoharis Theoharis, Auteur ; Georgios Thanellas, Auteur ; et al., Auteur Année de publication : 2018 Article en page(s) : pp 1 - 9 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes descripteurs IGN] analyse comparative [Termes descripteurs IGN] analyse de sensibilité [Termes descripteurs IGN] analyse spatiale [Termes descripteurs IGN] classification barycentrique [Termes descripteurs IGN] classification par séparateurs à vaste marge [Termes descripteurs IGN] codage [Termes descripteurs IGN] détection de piéton [Termes descripteurs IGN] discrétisation spatiale [Termes descripteurs IGN] distribution de Fisher [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] échantillonnage de données [Termes descripteurs IGN] image à basse résolution [Termes descripteurs IGN] reconnaissance de formes Résumé : (auteur) Range-based pedestrian recognition is instrumental towards the development of autonomous driving and driving assistance systems. This work introduces encoding methods for pedestrian recognition, based on statistical shape analysis of 3D LIDAR data. The proposed approach has two variants, based on the encoding of local shape descriptors either in a spatially agnostic or spatially sensitive fashion. The latter method derives more detailed cues, by enriching the ‘gross’ information reflected by overall statistics of local shape descriptors, with ‘fine-grained’ information reflected by statistics associated with spatial clusters. Experiments on artificial LIDAR datasets, which include challenging samples, as well as on a large scale dataset of real LIDAR data, lead to the conclusion that both variants of the proposed approach (i) obtain high recognition accuracy, (ii) are robust against low-resolution sampling, (iii) are robust against increasing distance, and (iv) are robust against non-standard shapes and poses. On the other hand, the spatially-sensitive variant is more robust against partial occlusion and bad clustering. Numéro de notice : A2018-586 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.cviu.2018.06.001 date de publication en ligne : 15/06/2018 En ligne : https://www.sciencedirect.com/science/article/pii/S1077314218300766 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92439 [article]

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)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 56 n° 2 (February 2018) . - pp 770 - 779 Titre : Fine-grained object recognition and zero-shot learning in remote sensing imagery Type de document : Article/Communication Auteurs : Gencer Sumbul, Auteur ; Ramazan Gokberk Cinbis, Auteur ; Selim Aksoy, Auteur Année de publication : 2018 Article en page(s) : pp 770 - 779 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] arbre urbain [Termes descripteurs IGN] image numérique [Termes descripteurs IGN] inférence [Termes descripteurs IGN] reconnaissance de formes [Termes descripteurs IGN] réseau neuronal convolutif Résumé : (Auteur) Fine-grained object recognition that aims to identify the type of an object among a large number of subcategories is an emerging application with the increasing resolution that exposes new details in image data. Traditional fully supervised algorithms fail to handle this problem where there is low between-class variance and high within-class variance for the classes of interest with small sample sizes. We study an even more extreme scenario named zero-shot learning (ZSL) in which no training example exists for some of the classes. ZSL aims to build a recognition model for new unseen categories by relating them to seen classes that were previously learned. We establish this relation by learning a compatibility function between image features extracted via a convolutional neural network and auxiliary information that describes the semantics of the classes of interest by using training samples from the seen classes. Then, we show how knowledge transfer can be performed for the unseen classes by maximizing this function during inference. We introduce a new data set that contains 40 different types of street trees in 1-ft spatial resolution aerial data, and evaluate the performance of this model with manually annotated attributes, a natural language model, and a scientific taxonomy as auxiliary information. The experiments show that the proposed model achieves 14.3% recognition accuracy for the classes with no training examples, which is significantly better than a random guess accuracy of 6.3% for 16 test classes, and three other ZSL algorithms. Numéro de notice : A2018-190 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2017.2754648 date de publication en ligne : 18/10/2017 En ligne : https://doi.org/10.1109/TGRS.2017.2754648 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89855 [article]

Recognition of building group patterns in topographic maps based on graph partitioning and random forest / Xianjin He in ISPRS Journal of photogrammetry and remote sensing, vol 136 (February 2018)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 136 (February 2018) . - pp 26 - 40 Titre : Recognition of building group patterns in topographic maps based on graph partitioning and random forest Type de document : Article/Communication Auteurs : Xianjin He, Auteur ; Xinchang Zhang, Auteur ; Qinchuan Xin, Auteur Année de publication : 2018 Article en page(s) : pp 26 - 40 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes descripteurs IGN] apprentissage automatique [Termes descripteurs IGN] bati [Termes descripteurs IGN] graphe [Termes descripteurs IGN] Kouangtoung (Chine) [Termes descripteurs IGN] partitionnement [Termes descripteurs IGN] reconnaissance de formes [Termes descripteurs IGN] ville [Vedettes matières IGN] Généralisation Résumé : (Auteur) Recognition of building group patterns (i.e., the arrangement and form exhibited by a collection of buildings at a given mapping scale) is important to the understanding and modeling of geographic space and is hence essential to a wide range of downstream applications such as map generalization. Most of the existing methods develop rigid rules based on the topographic relationships between building pairs to identify building group patterns and thus their applications are often limited. This study proposes a method to identify a variety of building group patterns that allow for map generalization. The method first identifies building group patterns from potential building clusters based on a machine-learning algorithm and further partitions the building clusters with no recognized patterns based on the graph partitioning method. The proposed method is applied to the datasets of three cities that are representative of the complex urban environment in Southern China. Assessment of the results based on the reference data suggests that the proposed method is able to recognize both regular (e.g., the collinear, curvilinear, and rectangular patterns) and irregular (e.g., the L-shaped, H-shaped, and high-density patterns) building group patterns well, given that the correctness values are consistently nearly 90% and the completeness values are all above 91% for three study areas. The proposed method shows promises in automated recognition of building group patterns that allows for map generalization. Numéro de notice : A2018-073 Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2017.12.001 En ligne : https://doi.org/10.1016/j.isprsjprs.2017.12.001 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89433 [article]

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A typification method for linear pattern in urban building generalisation / Xianyong Gong in Geocarto international, vol 33 n° 2 (February 2018)  

Public [article]  inGeocarto international > vol 33 n° 2 (February 2018) . - pp 189 - 207 Titre : A typification method for linear pattern in urban building generalisation Type de document : Article/Communication Auteurs : Xianyong Gong, Auteur ; Fang Wu, Auteur Année de publication : 2018 Article en page(s) : pp 189 - 207 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes descripteurs IGN] généralisation cartographique automatisée [Termes descripteurs IGN] généralisation du bâti [Termes descripteurs IGN] itération [Termes descripteurs IGN] modèle linéaire [Termes descripteurs IGN] reconnaissance de formes [Termes descripteurs IGN] triangulation de Delaunay [Termes descripteurs IGN] typification [Vedettes matières IGN] Généralisation Résumé : (Auteur) This paper presents a typification method for linear pattern in urban building generalization. The proposed method includes two processes. Firstly, structural knowledge in terms of linear pattern is detected using a two-step algorithm taking the advantages of Gestalt visual perception, computational geometry and graph theory. Spatial neighbourhood is captured using interpolated constrained Delaunay triangulation and the resulting proximity graph is pruned to be heterogeneous to get acceptable linear patterns with regard to Gestalt visual perception. Then, a typification strategy is proposed, in which typification is regarded as a progressive and iterative process consisting of elimination, exaggeration and displacement. The typification strategy iteratively executes eliminating the building with minimum overall effect, exaggerating remaining buildings considering key location and spatial characteristics and displacing them to preserve the linear pattern until elimination quantity is satisfied. Experiments show that this proposed strategy is effective and linear patterns are guaranteed with correctness and completeness. Numéro de notice : A2018-034 Thématique : GEOMATIQUE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2016.1240718 En ligne : https://doi.org/10.1080/10106049.2016.1240718 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89207 [article]

The analysis and measurement of building patterns using texton co-occurrence matrices / Wenhao Yu in International journal of geographical information science IJGIS, vol 31 n° 5-6 (May-June 2017)  

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Télédétection pour l'observation des surfaces continentales, ch. 6. Méthodes de traitement de données lidar / Clément Mallet (2017)

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A novel computer-aided tree species identification method based on burst wind segmentation of 3D bark textures / Alice Ahlem Othmani in Machine Vision and Applications, vol 27 n° 5 (July 2016)  

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Grid pattern recognition in road networks using the C4.5 algorithm / Jing Tian in Cartography and Geographic Information Science, Vol 43 n° 3 (June 2016)  

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Automatic keyline recognition and 3D reconstruction for quasi-planar façades in close-range images / Chang Li in Photogrammetric record, vol 31 n° 153 (March - May 2016)

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Improved salient feature-based approach for automatically separating photosynthetic and nonphotosynthetic components within terrestrial Lidar point cloud data of forest canopies / Lixia Ma in IEEE Transactions on geoscience and remote sensing, vol 54 n° 2 (February 2016)  

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Construction de modèles 3D à partir de données vidéo fisheye / Julien Moreau (2016)  

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Remote Sensing Observations of Continental Surfaces, ch. 6. Airborne lidar data processing / Clément Mallet (2016)  

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Automatic identification of building types based on topographic databases – a comparison of different data sources / Robert Hecht in International journal of cartography, vol 1 n° 1 (August 2015)  

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Combining top-down and bottom-up approaches for building detection in a single very high resolution satellite image / Mahmoud Mohammed Sidi Youssef (2014)  

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