Descripteur
Termes IGN > informatique > génie logiciel > programmation informatique
programmation informatiqueSynonyme(s)développement informatique |
Documents disponibles dans cette catégorie (682)
Ajouter le résultat dans votre panier
Visionner les documents numériques
Affiner la recherche Interroger des sources externes
Etendre la recherche sur niveau(x) vers le bas
An area merging method in map generalization considering typical characteristics of structured geographic objects / Chengming Li in Cartography and Geographic Information Science, vol 48 n° 3 (May 2021)
[article]
Titre : An area merging method in map generalization considering typical characteristics of structured geographic objects Type de document : Article/Communication Auteurs : Chengming Li, Auteur ; Yong Yin, Auteur ; Pengda Wu, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 210 - 224 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] Chine
[Termes IGN] conflit d'intégration
[Termes IGN] détection de contours
[Termes IGN] fusion de données
[Termes IGN] généralisation automatique de données
[Termes IGN] objet géographique zonal
[Termes IGN] occupation du sol
[Termes IGN] programmation adaptée à l'objet
[Termes IGN] structure spatiale
[Termes IGN] tessellation
[Termes IGN] ville
[Termes IGN] zone tampon
[Vedettes matières IGN] GénéralisationRésumé : (auteur) Merging is an important operation in the map generalization of land-cover and other coverages. We define structured geographic objects as collections of adjacent areas with homogeneous semantics that are regularly arranged as spatial structures. Existing studies have concentrated on unstructured objects, which will lead to the structured ones losing part or even most of the typical characteristics during merging. Therefore, as a supplement to the existing mature merging method, a targeted method was proposed in this paper to address the merging problem of structured geographic objects. First, structured geographic objects were classified into four typical patterns, and they were identified automatically according to seven spatial structure parameters. Second, a Miter-type buffer transformation was introduced to extract the overall boundary of structured geographic objects, and areas inside the overall boundary were processed with the most appropriate merging operations for their pattern. Finally, the corresponding merged results of structured geographic objects were inserted back into the merged result of the original land-cover data by using the NOT operation, and the spatial conflicts near the boundary were adjusted. We test our method for a dataset of geographical census data for a city in China. The experimental results revealed that compared with state-of-the-art method, the proposed method produces more reasonable generalization result by effectively identifying and maintaining the typical spatial structures; moreover, the proposed method also preserves the planar tessellation characteristic of land-cover data and the balance of area variation in each land-cover class. Numéro de notice : A2021-489 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/15230406.2020.1863862 Date de publication en ligne : 19/02/2021 En ligne : https://doi.org/10.1080/15230406.2020.1863862 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97530
in Cartography and Geographic Information Science > vol 48 n° 3 (May 2021) . - pp 210 - 224[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 032-2021031 RAB Revue Centre de documentation En réserve L003 Disponible Learning deep semantic segmentation network under multiple weakly-supervised constraints for cross-domain remote sensing image semantic segmentation / Yansheng Li in ISPRS Journal of photogrammetry and remote sensing, vol 175 (May 2021)
[article]
Titre : Learning deep semantic segmentation network under multiple weakly-supervised constraints for cross-domain remote sensing image semantic segmentation Type de document : Article/Communication Auteurs : Yansheng Li, Auteur ; Te Shi, Auteur ; Yongjun Zhang, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 20 - 33 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] classification semi-dirigée
[Termes IGN] données d'entrainement (apprentissage automatique)
[Termes IGN] programmation par contraintes
[Termes IGN] segmentation sémantiqueRésumé : (auteur) Due to its wide applications, remote sensing (RS) image semantic segmentation has attracted increasing research interest in recent years. Benefiting from its hierarchical abstract ability, the deep semantic segmentation network (DSSN) has achieved tremendous success on RS image semantic segmentation and has gradually become the mainstream technology. However, the superior performance of DSSN highly depends on two conditions: (I) massive quantities of labeled training data exist; (II) the testing data seriously resemble the training data. In actual RS applications, it is difficult to fully meet these conditions due to the RS sensor variation and the distinct landscape variation in different geographic locations. To make DSSN fit the actual RS scenario, this paper exploits the cross-domain RS image semantic segmentation task, which means that DSSN is trained on one labeled dataset (i.e., the source domain) but is tested on another varied dataset (i.e., the target domain). In this setting, the performance of DSSN is inevitably very limited due to the data shift between the source and target domains. To reduce the disadvantageous influence of data shift, this paper proposes a novel objective function with multiple weakly-supervised constraints to learn DSSN for cross-domain RS image semantic segmentation. Through carefully examining the characteristics of cross-domain RS image semantic segmentation, multiple weakly-supervised constraints include the weakly-supervised transfer invariant constraint (WTIC), weakly-supervised pseudo-label constraint (WPLC) and weakly-supervised rotation consistency constraint (WRCC). Specifically, DualGAN is recommended to conduct unsupervised style transfer between the source and target domains to carry out WTIC. To make full use of the merits of multiple constraints, this paper presents a dynamic optimization strategy that dynamically adjusts the constraint weights of the objective function during the training process. With full consideration of the characteristics of the cross-domain RS image semantic segmentation task, this paper gives two cross-domain RS image semantic segmentation settings: (I) variation in geographic location and (II) variation in both geographic location and imaging mode. Extensive experiments demonstrate that our proposed method remarkably outperforms the state-of-the-art methods under both of these settings. The collected datasets and evaluation benchmarks have been made publicly available online (https://github.com/te-shi/MUCSS). Numéro de notice : A2021-261 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2021.02.009 Date de publication en ligne : 06/03/2021 En ligne : https://doi.org/10.1016/j.isprsjprs.2021.02.009 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97302
in ISPRS Journal of photogrammetry and remote sensing > vol 175 (May 2021) . - pp 20 - 33[article]Exemplaires(3)
Code-barres Cote Support Localisation Section Disponibilité 081-2021051 SL Revue Centre de documentation Revues en salle Disponible 081-2021052 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt 081-2021053 DEP-RECP Revue Saint-Mandé Dépôt en unité Exclu du prêt Restituer les bidonvilles de Nanterre : l’apport d’un outil de visualisation 3D à un projet de sciences sociales / Paul Lecat in Humanités numériques, n° 3 (2021)
[article]
Titre : Restituer les bidonvilles de Nanterre : l’apport d’un outil de visualisation 3D à un projet de sciences sociales Titre original : Restoring the Shantytowns of Nanterre: Benefits of a 3D Visualisation Tool for a Social Sciences Project Type de document : Article/Communication Auteurs : Paul Lecat, Auteur ; Emile Blettery , Auteur ; Laetitia Delavoipiere, Auteur ; Frédéric Saly-Giocanti, Auteur ; Sylvaine Conord, Auteur ; Valérie Gouet-Brunet , Auteur ; Alexandre Devaux , Auteur ; Mathieu Brédif , Auteur ; Frédéric Moret, Auteur Année de publication : 2021 Projets : ITowns / Paparoditis, Nicolas, Alegoria / Gouet-Brunet, Valérie Article en page(s) : n° 1946 Note générale : bibliographie Langues : Français (fre) Descripteur : [Termes IGN] analyse spatio-temporelle
[Termes IGN] bidonville
[Termes IGN] image aérienne
[Termes IGN] morphologie urbaine
[Termes IGN] Nanterre
[Termes IGN] outil logiciel
[Termes IGN] sciences sociales
[Termes IGN] SIG 3D
[Termes IGN] sociologie
[Termes IGN] visualisation 3D
[Vedettes matières IGN] GéovisualisationRésumé : (auteur) Au milieu des années 1950 surgissent à Nanterre les premières cabanes de fortune abritant des travailleurs algériens. Bientôt, ces baraques informelles s’agrègent et finissent par former des ensembles urbains, présentés et administrés comme des bidonvilles, et la ville de Nanterre y est alors durablement associée. Cet article se propose de revenir sur une expérience de recherche interdisciplinaire autour de cet objet d’étude. Des chercheurs en histoire et en sociologie urbaine ont collaboré avec des informaticiens de l’IGN afin d’utiliser et d’enrichir une plateforme de spatialisation et de visualisation de données hétérogènes pour documenter l’histoire de ces bidonvilles et comprendre la formation et la permanence de ces lieux dans la mémoire collective actuelle. Numéro de notice : A2021-308 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueNat DOI : 10.4000/revuehn.1946 Date de publication en ligne : 01/05/2021 En ligne : https://doi.org/10.4000/revuehn.1946 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97709
in Humanités numériques > n° 3 (2021) . - n° 1946[article]Unsupervised multi-level feature extraction for improvement of hyperspectral classification / Qiaoqiao Sun in Remote sensing, vol 13 n° 8 (April-2 2021)
[article]
Titre : Unsupervised multi-level feature extraction for improvement of hyperspectral classification Type de document : Article/Communication Auteurs : Qiaoqiao Sun, Auteur ; Xuefeng Liu, Auteur ; Salah Bourennane, Auteur Année de publication : 2021 Article en page(s) : n° 1602 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage profond
[Termes IGN] classification non dirigée
[Termes IGN] codage
[Termes IGN] convolution (signal)
[Termes IGN] déconvolution
[Termes IGN] échantillonnage d'image
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] image hyperspectrale
[Termes IGN] observation multiniveauxRésumé : (auteur) Deep learning models have strong abilities in learning features and they have been successfully applied in hyperspectral images (HSIs). However, the training of most deep learning models requires labeled samples and the collection of labeled samples are labor-consuming in HSI. In addition, single-level features from a single layer are usually considered, which may result in the loss of some important information. Using multiple networks to obtain multi-level features is a solution, but at the cost of longer training time and computational complexity. To solve these problems, a novel unsupervised multi-level feature extraction framework that is based on a three dimensional convolutional autoencoder (3D-CAE) is proposed in this paper. The designed 3D-CAE is stacked by fully 3D convolutional layers and 3D deconvolutional layers, which allows for the spectral-spatial information of targets to be mined simultaneously. Besides, the 3D-CAE can be trained in an unsupervised way without involving labeled samples. Moreover, the multi-level features are directly obtained from the encoded layers with different scales and resolutions, which is more efficient than using multiple networks to get them. The effectiveness of the proposed multi-level features is verified on two hyperspectral data sets. The results demonstrate that the proposed method has great promise in unsupervised feature learning and can help us to further improve the hyperspectral classification when compared with single-level features. Numéro de notice : A2021-380 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/rs13081602 Date de publication en ligne : 20/04/2021 En ligne : https://doi.org/10.3390/rs13081602 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97628
in Remote sensing > vol 13 n° 8 (April-2 2021) . - n° 1602[article]Graph convolutional autoencoder model for the shape coding and cognition of buildings in maps / Xiongfeng Yan in International journal of geographical information science IJGIS, vol 35 n° 3 (March 2021)
[article]
Titre : Graph convolutional autoencoder model for the shape coding and cognition of buildings in maps Type de document : Article/Communication Auteurs : Xiongfeng Yan, Auteur ; Tinghua Ai, Auteur ; Min Yang, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 490 - 512 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] apprentissage non-dirigé
[Termes IGN] apprentissage profond
[Termes IGN] codage
[Termes IGN] données vectorielles
[Termes IGN] graphe
[Termes IGN] mesure géométrique
[Termes IGN] modélisation du bâti
[Termes IGN] représentation cognitive
[Termes IGN] représentation spatialeRésumé : (auteur) The shape of a geospatial object is an important characteristic and a significant factor in spatial cognition. Existing shape representation methods for vector-structured objects in the map space are mainly based on geometric and statistical measures. Considering that shape is complicated and cognitively related, this study develops a learning strategy to combine multiple features extracted from its boundary and obtain a reasonable shape representation. Taking building data as example, this study first models the shape of a building using a graph structure and extracts multiple features for each vertex based on the local and regional structures. A graph convolutional autoencoder (GCAE) model comprising graph convolution and autoencoder architecture is proposed to analyze the modeled graph and realize shape coding through unsupervised learning. Experiments show that the GCAE model can produce a cognitively compliant shape coding, with the ability to distinguish different shapes. It outperforms existing methods in terms of similarity measurements. Furthermore, the shape coding is experimentally proven to be effective in representing the local and global characteristics of building shape in application scenarios such as shape retrieval and matching. Numéro de notice : A2021-166 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2020.1768260 Date de publication en ligne : 25/05/2020 En ligne : https://doi.org/10.1080/13658816.2020.1768260 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97100
in International journal of geographical information science IJGIS > vol 35 n° 3 (March 2021) . - pp 490 - 512[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 079-2021031 SL Revue Centre de documentation Revues en salle Disponible Using geometric constraints to improve performance of image classifiers for automatic segmentation of traffic signs / Roholah Yazdan in Geomatica, vol 75 n° 1 (Mars 2021)PermalinkPermalinkClustering et apprentissage profond sous contraintes pour l’analyse de séries temporelles : Application à l’analyse temporelle incrémentale en télédétection / Baptiste Lafabregue (2021)PermalinkPermalinkPermalinkMise en place d’une infrastructure de données spatiales sur le risque de piqures de tiques / Lilian Calas (2021)PermalinkFlex-ER: A platform to evaluate interaction techniques for immersive visualizations / María-Jesús Lobo in Proceedings of the ACM on Human-Computer Interaction, Vol 4 (November 2020)PermalinkUrban Wi-Fi fingerprinting along a public transport route / Guenther Retscher in Journal of applied geodesy, vol 14 n° 4 (October 2020)PermalinkLocal terrain modification method considering physical feature constraints for vector elements / Jiangfeng She in Cartography and Geographic Information Science, Vol 47 n° 5 (September 2020)PermalinkProvably consistent distributed Delaunay triangulation / Mathieu Brédif in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-2-2020 (August 2020)Permalink