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Titre : Generating urban morphologies at large scales Type de document : Article/Communication Auteurs : Juste Raimbault, Auteur ; Julien Perret , Auteur Editeur : Cambridge [Massachusetts - Etats-Unis] : MIT Press Année de publication : 2019 Projets : DynamiCity / Conférence : Alife 2019, Conference on Artificial life 29/07/2019 02/08/2019 Newcastle Royaume-Uni Open Access Proceedings Importance : 8 p. Format : 21 x 30 cm Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] Europe (géographie politique)
[Termes IGN] grande échelle
[Termes IGN] indicateur
[Termes IGN] modèle numérique
[Termes IGN] modélisation de processus
[Termes IGN] morphologie mathématique
[Termes IGN] morphologie urbaine
[Termes IGN] OpenStreetMap
[Termes IGN] reconnaissance de formes
[Termes IGN] zone urbaineRésumé : (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 Affiliation des auteurs : LASTIG COGIT+Ext (2012-2019) Autre URL associée : vers HAL Thématique : GEOMATIQUE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1162/isal_a_00159 En ligne : https://doi.org/10.1162/isal_a_00159 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93262 Documents numériques
en open access
Generating urban morphologies at large scales - pdf auteurAdobe Acrobat PDF Spatially sensitive statistical shape analysis for pedestrian recognition from LIDAR data / Michalis A. Savelonas in Computer Vision and image understanding, vol 171 (June 2018)
[article]
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 IGN] analyse comparative
[Termes IGN] analyse de sensibilité
[Termes IGN] analyse spatiale
[Termes IGN] classification barycentrique
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] codage
[Termes IGN] détection de piéton
[Termes IGN] discrétisation spatiale
[Termes IGN] distribution de Fisher
[Termes IGN] données lidar
[Termes IGN] échantillonnage de données
[Termes IGN] image à basse résolution
[Termes IGN] reconnaissance de formesRésumé : (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 Affiliation des auteurs : non IGN 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
in Computer Vision and image understanding > vol 171 (June 2018) . - pp 1 - 9[article]Do semantic parts emerge in convolutional neural networks? / Abel Gonzalez-Garcia in International journal of computer vision, vol 126 n° 5 (May 2018)
[article]
Titre : Do semantic parts emerge in convolutional neural networks? Type de document : Article/Communication Auteurs : Abel Gonzalez-Garcia, Auteur ; Davide Modolo, Auteur ; Vittorio Ferrari, Auteur Année de publication : 2018 Article en page(s) : pp 476 - 494 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle
[Termes IGN] reconnaissance d'objets
[Termes IGN] rectangle englobant minimum
[Termes IGN] réseau neuronal convolutif
[Termes IGN] segmentation sémantiqueRésumé : (Auteur) Semantic object parts can be useful for several visual recognition tasks. Lately, these tasks have been addressed using Convolutional Neural Networks (CNN), achieving outstanding results. In this work we study whether CNNs learn semantic parts in their internal representation. We investigate the responses of convolutional filters and try to associate their stimuli with semantic parts. We perform two extensive quantitative analyses. First, we use ground-truth part bounding-boxes from the PASCAL-Part dataset to determine how many of those semantic parts emerge in the CNN. We explore this emergence for different layers, network depths, and supervision levels. Second, we collect human judgements in order to study what fraction of all filters systematically fire on any semantic part, even if not annotated in PASCAL-Part. Moreover, we explore several connections between discriminative power and semantics. We find out which are the most discriminative filters for object recognition, and analyze whether they respond to semantic parts or to other image patches. We also investigate the other direction: we determine which semantic parts are the most discriminative and whether they correspond to those parts emerging in the network. This enables to gain an even deeper understanding of the role of semantic parts in the network. Numéro de notice : A2018-408 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s11263-017-1048-0 Date de publication en ligne : 17/10/2017 En ligne : https://doi.org/10.1007/s11263-017-1048-0 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90882
in International journal of computer vision > vol 126 n° 5 (May 2018) . - pp 476 - 494[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)
[article]
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 IGN] arbre urbain
[Termes IGN] image numérique
[Termes IGN] inférence
[Termes IGN] reconnaissance de formes
[Termes IGN] réseau neuronal convolutifRésumé : (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 Affiliation des auteurs : non IGN 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
in IEEE Transactions on geoscience and remote sensing > vol 56 n° 2 (February 2018) . - pp 770 - 779[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)
[article]
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 IGN] apprentissage automatique
[Termes IGN] bati
[Termes IGN] graphe
[Termes IGN] Kouangtoung (Chine)
[Termes IGN] partitionnement
[Termes IGN] reconnaissance de formes
[Termes IGN] ville
[Vedettes matières IGN] GénéralisationRésumé : (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 Affiliation des auteurs : non IGN 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
in ISPRS Journal of photogrammetry and remote sensing > vol 136 (February 2018) . - pp 26 - 40[article]Réservation
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