Descripteur
Documents disponibles dans cette catégorie (1332)
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
Forest change detection in incomplete satellite images with deep neural networks / Salman H. Khan in IEEE Transactions on geoscience and remote sensing, vol 55 n° 9 (September 2017)
[article]
Titre : Forest change detection in incomplete satellite images with deep neural networks Type de document : Article/Communication Auteurs : Salman H. Khan, Auteur ; Xuming He, Auteur ; Fatih Porikli, Auteur ; Mohammed Bennamoun, Auteur Année de publication : 2017 Article en page(s) : pp 5407 - 5423 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse diachronique
[Termes IGN] analyse multirésolution
[Termes IGN] apprentissage profond
[Termes IGN] détection de changement
[Termes IGN] forêt
[Termes IGN] réflectance de surface
[Termes IGN] réseau neuronal artificiel
[Termes IGN] retouche
[Termes IGN] surveillance de la végétationRésumé : (Auteur) Land cover change monitoring is an important task from the perspective of regional resource monitoring, disaster management, land development, and environmental planning. In this paper, we analyze imagery data from remote sensing satellites to detect forest cover changes over a period of 29 years (1987-2015). Since the original data are severely incomplete and contaminated with artifacts, we first devise a spatiotemporal inpainting mechanism to recover the missing surface reflectance information. The spatial filling process makes use of the available data of the nearby temporal instances followed by a sparse encoding-based reconstruction. We formulate the change detection task as a region classification problem. We build a multiresolution profile (MRP) of the target area and generate a candidate set of bounding-box proposals that enclose potential change regions. In contrast to existing methods that use handcrafted features, we automatically learn region representations using a deep neural network in a data-driven fashion. Based on these highly discriminative representations, we determine forest changes and predict their onset and offset timings by labeling the candidate set of proposals. Our approach achieves the state-of-the-art average patch classification rate of 91.6% (an improvement of ~16%) and the mean onset/offset prediction error of 4.9 months (an error reduction of five months) compared with a strong baseline. We also qualitatively analyze the detected changes in the unlabeled image regions, which demonstrate that the proposed forest change detection approach is scalable to new regions. Numéro de notice : A2017-663 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2017.2707528 En ligne : http://dx.doi.org/10.1109/TGRS.2017.2707528 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=87105
in IEEE Transactions on geoscience and remote sensing > vol 55 n° 9 (September 2017) . - pp 5407 - 5423[article]Insight provenance for spatiotemporal visual analytics : Theory, review, and guidelines / Andreas Hall in Journal of Spatial Information Science (JoSIS), n° 15 (September 2017)
[article]
Titre : Insight provenance for spatiotemporal visual analytics : Theory, review, and guidelines Type de document : Article/Communication Auteurs : Andreas Hall, Auteur ; Paula Ahonen-Rainio, Auteur ; Kirsi Virrantaus, Auteur Année de publication : 2017 Article en page(s) : pp 65 - 88 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] analyse géovisuelle
[Termes IGN] cadre conceptuel
[Termes IGN] données spatiotemporelles
[Termes IGN] orientations
[Termes IGN] raisonnement spatial
[Termes IGN] représentation mentale spatiale
[Vedettes matières IGN] GéovisualisationRésumé : (Auteur) Research on provenance, which focuses on different ways to describe and record the history of changes and advances made throughout an analysis process, is an integral part of visual analytics. This paper focuses on providing the provenance of insight and rationale through visualizations while emphasizing, first, that this entails a profound understanding of human cognition and reasoning and that, second, the special nature of spatiotemporal data needs to be acknowledged in this process. A recently proposed human reasoning framework for spatiotemporal analysis, and four guidelines for the creation of visualizations that provide the provenance of insight and rationale published in relation to that framework, work as a starting point for this paper. While these guidelines are quite abstract, this paper set out to create a set of more concrete guidelines. On the basis of a review of available provenance solutions, this paper identifies a set of key features that are of relevance when providing the provenance of insight and rationale and, on the basis of these features, produces a new set of complementary guidelines that are more practically oriented than the original ones. Together, these two sets of guidelines provide both a theoretical and practical approach to the problem of providing the provenance of insight and rationale. Providing these kinds of guidelines represents a new approach in provenance research. Numéro de notice : A2017-822 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.5311/JOSIS.2017.15.337 En ligne : https://doi.org/10.5311/JOSIS.2017.15.337 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89307
in Journal of Spatial Information Science (JoSIS) > n° 15 (September 2017) . - pp 65 - 88[article]Mapping theories of transformative learning / Daniel Casebeer in Cartographica, vol 52 n° 3 (Fall 2017)
[article]
Titre : Mapping theories of transformative learning Type de document : Article/Communication Auteurs : Daniel Casebeer, Auteur ; Jessica Mann, Auteur Année de publication : 2017 Article en page(s) : pp 233 – 237 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Cartographie thématique
[Termes IGN] apprentissage automatique
[Termes IGN] apprentissage par transformation
[Termes IGN] géographie sociale
[Termes IGN] représentation cartographique
[Termes IGN] segmentation sémantiqueRésumé : (auteur) The purpose of this study is to demonstrate the utility of social cartography for mapping theories of transformative learning. Since the 1980s, several alternative conceptions of transformative learning have emerged to challenge the dominance of Jack Mezirow's psychocritical perspective. Rather than positioning these theories in opposition to one another, this study uses textual analysis and a phenomenographic method to situate them in a heterotopic space where researchers can orient themselves as they encounter new intellectual and representational tasks brought on by the diversification of the field. Whether the map is accepted as a metaphorical curiosity or more as a literal representation, it can reveal perceived or acknowledged theoretical relationships while identifying issues in transformative education that still need to be addressed. Numéro de notice : A2017-734 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.3138/cart.52.3.3956 En ligne : https://doi.org/10.3138/cart.52.3.3956 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=88373
in Cartographica > vol 52 n° 3 (Fall 2017) . - pp 233 – 237[article]Réservation
Réserver ce documentExemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 031-2017031 SL Revue Centre de documentation Revues en salle Disponible Recurrent neural networks to correct satellite image classification maps / Emmanuel Maggiori in IEEE Transactions on geoscience and remote sensing, vol 55 n° 9 (September 2017)
[article]
Titre : Recurrent neural networks to correct satellite image classification maps Type de document : Article/Communication Auteurs : Emmanuel Maggiori, Auteur ; Guillaume Charpiat, Auteur ; Yuliya Tarabalka, Auteur ; Pierre Alliez, Auteur Année de publication : 2017 Article en page(s) : pp 4962 - 4971 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage automatique
[Termes IGN] classification par réseau neuronal
[Termes IGN] convolution (signal)
[Termes IGN] itération
[Termes IGN] réseau neuronal convolutifRésumé : (Auteur) While initially devised for image categorization, convolutional neural networks (CNNs) are being increasingly used for the pixelwise semantic labeling of images. However, the proper nature of the most common CNN architectures makes them good at recognizing but poor at localizing objects precisely. This problem is magnified in the context of aerial and satellite image labeling, where a spatially fine object outlining is of paramount importance. Different iterative enhancement algorithms have been presented in the literature to progressively improve the coarse CNN outputs, seeking to sharpen object boundaries around real image edges. However, one must carefully design, choose, and tune such algorithms. Instead, our goal is to directly learn the iterative process itself. For this, we formulate a generic iterative enhancement process inspired from partial differential equations, and observe that it can be expressed as a recurrent neural network (RNN). Consequently, we train such a network from manually labeled data for our enhancement task. In a series of experiments, we show that our RNN effectively learns an iterative process that significantly improves the quality of satellite image classification maps. Numéro de notice : A2017-659 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2017.2697453 En ligne : http://dx.doi.org/10.1109/TGRS.2017.2697453 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=87070
in IEEE Transactions on geoscience and remote sensing > vol 55 n° 9 (September 2017) . - pp 4962 - 4971[article]Remote sensing scene classification by unsupervised representation learning / Xiaoqiang Lu in IEEE Transactions on geoscience and remote sensing, vol 55 n° 9 (September 2017)
[article]
Titre : Remote sensing scene classification by unsupervised representation learning Type de document : Article/Communication Auteurs : Xiaoqiang Lu, Auteur ; Xiangtao Zheng, Auteur ; Yuan Yuan, Auteur Année de publication : 2017 Article en page(s) : pp 5148 - 5157 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage non-dirigé
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] déconvolution
[Termes IGN] image à haute résolution
[Termes IGN] réseau neuronal artificiel
[Termes IGN] scène
[Termes IGN] Sydney (Nouvelle-Galles du Sud)Résumé : (Auteur) With the rapid development of the satellite sensor technology, high spatial resolution remote sensing (HSR) data have attracted extensive attention in military and civilian applications. In order to make full use of these data, remote sensing scene classification becomes an important and necessary precedent task. In this paper, an unsupervised representation learning method is proposed to investigate deconvolution networks for remote sensing scene classification. First, a shallow weighted deconvolution network is utilized to learn a set of feature maps and filters for each image by minimizing the reconstruction error between the input image and the convolution result. The learned feature maps can capture the abundant edge and texture information of high spatial resolution images, which is definitely important for remote sensing images. After that, the spatial pyramid model (SPM) is used to aggregate features at different scales to maintain the spatial layout of HSR image scene. A discriminative representation for HSR image is obtained by combining the proposed weighted deconvolution model and SPM. Finally, the representation vector is input into a support vector machine to finish classification. We apply our method on two challenging HSR image data sets: the UCMerced data set with 21 scene categories and the Sydney data set with seven land-use categories. All the experimental results achieved by the proposed method outperform most state of the arts, which demonstrates the effectiveness of the proposed method. Numéro de notice : A2017-664 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2017.2702596 En ligne : http://dx.doi.org/10.1109/TGRS.2017.2702596 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=87103
in IEEE Transactions on geoscience and remote sensing > vol 55 n° 9 (September 2017) . - pp 5148 - 5157[article]SDE: A novel selective, discriminative and equalizing feature representation for visual recognition / Guo-Sen Xie in International journal of computer vision, vol 124 n° 2 (1 September 2017)PermalinkSIG et intelligence artificielle : quels développements et quel futur ? / Christian Carolin in Géomatique expert, n° 118 (septembre - octobre 2017)PermalinkUnsupervised domain adaptation for early detection of drought stress in hyperspectral images / P. Schmitter in ISPRS Journal of photogrammetry and remote sensing, vol 131 (September 2017)PermalinkFrom subpixel to superpixel : a novel fusion framework for hyperspectral image classification / Ting Lu in IEEE Transactions on geoscience and remote sensing, vol 55 n° 8 (August 2017)PermalinkLearning and transferring deep joint spectral–spatial features for hyperspectral classification / Jingxiang Yang in IEEE Transactions on geoscience and remote sensing, vol 55 n° 8 (August 2017)PermalinkLearning a discriminative distance metric with label consistency for scene classification / Yuebin Wang in IEEE Transactions on geoscience and remote sensing, vol 55 n° 8 (August 2017)PermalinkLearning sensor-specific spatial-spectral features of hyperspectral images via convolutional neural networks / Shaohui Mei in IEEE Transactions on geoscience and remote sensing, vol 55 n° 8 (August 2017)PermalinkReference data enhancement for geographic information retrieval using linked data / Tiago H. V. M. Moura in Transactions in GIS, vol 21 n° 4 (August 2017)PermalinkA relative evaluation of random forests for land cover mapping in an urban area / Di Shi in Photogrammetric Engineering & Remote Sensing, PERS, vol 83 n° 8 (August 2017)PermalinkSimultaneous extraction of roads and buildings in remote sensing imagery with convolutional neural networks / Rasha Alshehhi in ISPRS Journal of photogrammetry and remote sensing, vol 130 (August 2017)PermalinkAggregation-based information retrieval system for geospatial data catalogs / Javier Lacasta in International journal of geographical information science IJGIS, vol 31 n° 7-8 (July - August 2017)PermalinkMap the gap: alternative visualisations of geographic knowledge production / Margath Walker in Geo: Geography and Environment, vol 4 n°2 (July 2017)PermalinkA morphologically preserved multi-resolution TIN surface modeling and visualization method for virtual globes / Xianwei Zheng in ISPRS Journal of photogrammetry and remote sensing, vol 129 (July 2017)PermalinkLes observatoires territoriaux : Des outils de la société de la connaissance ? / Jean Philippe Tonneau in Revue internationale de géomatique, vol 27 n° 3 (juillet-septembre 2017)PermalinkPerSE : visual analytics for calendar related spatiotemporal periodicity detection and analysis / Brian Swedberg in Geoinformatica, vol 21 n° 3 (July - September 2017)PermalinkRobust point cloud classification based on multi-level semantic relationships for urban scenes / Qing Zhu in ISPRS Journal of photogrammetry and remote sensing, vol 129 (July 2017)PermalinkVers un observatoire agro-environnemental des territoires : Un système décisionnel multi-échelle pour le bassin de la Charente / Françoise Vernier in Revue internationale de géomatique, vol 27 n° 3 (juillet-septembre 2017)PermalinkSpatial thinking in archaeology: Is GIS the answer? / Gary Lock in Journal of archaeological science, vol 84 (August 2017)PermalinkAn investigation into challenges experienced when route planning, navigating and wayfinding / Erin Koletsis in International journal of cartography, vol 3 n° 1 (June 2017)PermalinkAutomatic GPS ionospheric amplitude and phase scintillation detectors using a machine learning algorithm / Yu Jiao in Inside GNSS, vol 12 n° 3 (May - June 2017)PermalinkCan a machine generate humanlike language descriptions for a remote sensing image? / Zhenwei Shi in IEEE Transactions on geoscience and remote sensing, vol 55 n° 6 (June 2017)PermalinkDIOGEN, a multi-level oriented model for cartographic generalization / Adrien Maudet in International journal of cartography, vol 3 n° 1 (June 2017)PermalinkLearning to diversify deep belief networks for hyperspectral image classification / Ping Zhong in IEEE Transactions on geoscience and remote sensing, vol 55 n° 6 (June 2017)PermalinkMonitoring mangrove biomass change in Vietnam using SPOT images and an object-based approach combined with machine learning algorithms / Lien T.H. Pham in ISPRS Journal of photogrammetry and remote sensing, vol 128 (June 2017)PermalinkA novel semisupervised active-learning algorithm for hyperspectral image classification / Zengmao Wang in IEEE Transactions on geoscience and remote sensing, vol 55 n° 6 (June 2017)PermalinkPlace and city : operationalizing sense of place and social capital in the urban context / Albert Acedo in Transactions in GIS, vol 21 n° 3 (June 2017)PermalinkVariable-scale maps in real-time generalisation using a quadtree data structure and space deforming algorithms / Pia Bereuter in International journal of cartography, vol 3 n° 1 (June 2017)PermalinkViCTOr : paysage virtuel pour explorer les dynamiques de la VIticulture et de la Consommation en TouRaine / Etienne Delay in Cybergeo, European journal of geography, n° 2017 ([01/06/2017])PermalinkInvestigating the potential of deep neural networks for large-scale classification of very high resolution satellite images / Tristan Postadjian in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol IV-1/W1 (May 2017)PermalinkAn intelligent spatial land use planning support system using socially rational agents / Seyed Moral Ghavami in International journal of geographical information science IJGIS, vol 31 n° 5-6 (May-June 2017)PermalinkAn ontology-driven multi-agent system for nautical chart generalization / Jingya Yan in Cartography and Geographic Information Science, Vol 44 n° 3 (May 2017)PermalinkAn unsupervised two-stage clustering approach for forest structure classification based on X-band InSAR data — A case study in complex temperate forest stands / Sahra Abdullahi in International journal of applied Earth observation and geoinformation, vol 57 (May 2017)PermalinkComplétion d'image exploitant des données multispectrales / Frédéric Bousefsaf in Revue Française de Photogrammétrie et de Télédétection, n° 215 (mai - août 2017)PermalinkDesign and evaluation of a geovisual analytics system for uncovering patterns in spatio-temporal event data / Anthony C. Robinson in Cartography and Geographic Information Science, Vol 44 n° 3 (May 2017)PermalinkDimensionality reduction and classification of hyperspectral images using ensemble discriminative local metric learning / Yanni Dong in IEEE Transactions on geoscience and remote sensing, vol 55 n° 5 (May 2017)PermalinkExploring spatiotemporal clusters based on extended kernel estimation methods / Jay Lee in International journal of geographical information science IJGIS, vol 31 n° 5-6 (May-June 2017)PermalinkLinking spatial named entities to the web of data for geographical analysis of historical texts / Pierre-Henri Paris in Journal of Map & Geography Libraries, vol 13 n° 1 ([01/05/2017])PermalinkModeling dynamic urban land-use change with geographical cellular automata and generalized pattern search-optimized rules / Yongjiu Feng in International journal of geographical information science IJGIS, vol 31 n° 5-6 (May-June 2017)PermalinkMotion priors based on goals hierarchies in pedestrian tracking applications / Francisco Madrigal in Machine Vision and Applications, vol 28 n° 3-4 (May 2017)PermalinkRule-guided human classification of Volunteered Geographic Information / Ahmed Loai Ali in ISPRS Journal of photogrammetry and remote sensing, vol 127 (May 2017)PermalinkSelf-taught feature learning for hyperspectral image classification / Ronald Kemker in IEEE Transactions on geoscience and remote sensing, vol 55 n° 5 (May 2017)PermalinkSuperpixel-based multitask learning framework for hyperspectral image classification / Sen Jia in IEEE Transactions on geoscience and remote sensing, vol 55 n° 5 (May 2017)PermalinkDeep supervised and contractive neural network for SAR image classification / Jie Geng in IEEE Transactions on geoscience and remote sensing, vol 55 n° 4 (April 2017)PermalinkIntegrating cellular automata and Markov techniques to generate urban development potential surface : a study on Kolkata agglomeration / Biswajit Mondal in Geocarto international, vol 32 n° 4 (April 2017)PermalinkSpatiotemporal downscaling approaches for monitoring 8-day 30 m actual evapotranspiration / Yinghai Ke in ISPRS Journal of photogrammetry and remote sensing, vol 126 (April 2017)PermalinkUnsupervised feature learning for land-use scene recognition / Jiayuan Fan in IEEE Transactions on geoscience and remote sensing, vol 55 n° 4 (April 2017)PermalinkClassifying natural-language spatial relation terms with random forest algorithm / Shihong Du in International journal of geographical information science IJGIS, vol 31 n° 3-4 (March-April 2017)PermalinkCognitively plausible representations for the alignment of sketch and geo-referenced maps / Sahib Jan in Journal of Spatial Information Science (JoSIS), n° 14 (March 2017)PermalinkDictionary learning-based feature-level domain adaptation for cross-scene hyperspectral image classification / Minchao Ye in IEEE Transactions on geoscience and remote sensing, vol 55 n° 3 (March 2017)PermalinkExtracting target spectrum for hyperspectral target detection : an adaptive weighted learning method using a self-completed background dictionary / Yubin Niu in IEEE Transactions on geoscience and remote sensing, vol 55 n° 3 (March 2017)PermalinkA hybrid genetic algorithm with local optimiser improves calibration of a vegetation change cellular automata model / Rachel Whitsed in International journal of geographical information science IJGIS, vol 31 n° 3-4 (March-April 2017)PermalinkPredicting the encoding of secondary diagnoses. An experience based on decision trees / Ghazar Chahbandarian in Ingénierie des systèmes d'information, ISI : Revue des sciences et technologies de l'information, RSTI, vol 22 n° 2 (mars - avril 2017)PermalinkTowards a knowledge base to support geoprocessing workflow development / Barbara Hofer in International journal of geographical information science IJGIS, vol 31 n° 3-4 (March-April 2017)PermalinkLe travail de la science et le numérique : Données, publications, plateformes / Direction de l'information scientifique et technique - CNRS (20/02/2017)PermalinkJoint sparse representation and multitask learning for hyperspectral target detection / Yuxiang Zhang in IEEE Transactions on geoscience and remote sensing, vol 55 n° 2 (February 2017)PermalinkStatistical Relational Learning of Grammar Rules for 3D Building Reconstruction / Youness Dehbi in Transactions in GIS, vol 21 n° 1 (February 2017)PermalinkAmélioration de la vitesse et de la qualité d'image du rendu basé image / Rodrigo Ortiz Cayón (2017)PermalinkPermalinkAnalyse et cartographie des sentiments dans des récits de vie de migrants / Catherine Dominguès (2017)PermalinkPermalinkAssessing the positional planimetric accuracy of DBpedia georeferenced resources / Abdelfettah Feliachi (2017)PermalinkCartographie de l'occupation des sols à partir de séries temporelles d'images satellitaires à hautes résolutions : identification et traitement des données mal étiquetées / Charlotte Pelletier (2017)PermalinkCentimetric absolute localization using Unmanned Aerial Vehicles with airborne photogrammetry and on-board GPS / Mehdi Daakir (2017)PermalinkContributions méthodologiques pour la caractérisation des milieux par imagerie optique et lidar / Nesrine Chehata (2017)PermalinkEtude et méthodes d'intégration et d'interaction de données 3D complexes type "nuages de points" vers un web SIG / Victor Lambert (2017)PermalinkEvaluating data stability in aggregation structures across spatial scales: revisiting the modifiable areal unit problem / Jonathan K. Nelson in Cartography and Geographic Information Science, Vol 44 n° 1 (January 2017)PermalinkPermalinkFusion of multi-temporal Sentinel-2 image series and very-high spatial resolution images for detection of urban areas / Cyril Wendl (2017)PermalinkPermalinkInterconnexion et visualisation de ressources géoréférencées du Web de données à l’aide d’un référentiel topographique de support / Abdelfettah Feliachi (2017)PermalinkPermalinkIonosphere probing with simultaneous GNSS radio occultations / Viet-Cuong Pham in GPS solutions, vol 21 n° 1 (January 2017)PermalinkJoint analysis of passive and active land surface responses for Global Precipitation Measurement / Iris de Gelis (2017)PermalinkLearning-based spatial-temporal superresolution mapping of forest cover with MODIS images / Yihang Zhang in IEEE Transactions on geoscience and remote sensing, vol 55 n° 1 (January 2017)PermalinkModèles géographiques avec le langage Mathematica / André Dauphiné (2017)PermalinkPermalinkModelling a dynamic forest fuelmarket focusing on wood chips: a spatial agent-based approach to simulate competition among heating plants in the province of Carinthia, Austria / Johannes Scholz in GI Forum, vol 2017 n° 1 ([01/01/2017])PermalinkNew iterative learning strategy to improve classification systems by using outlier detection techniques / Charlotte Pelletier (2017)PermalinkProgressive block graying and landmarks enhancing as intermediate representations between buildings and urban areas / Guillaume Touya (2017)PermalinkProjet CHOUCAS : Intégration de données hétérogènes et raisonnement spatial pour l'aide à la localisation des victimes en montagne / Ana-Maria Olteanu-Raimond (2017)PermalinkRandom-walker-based collaborative learning for hyperspectral image classification / Bin Sun in IEEE Transactions on geoscience and remote sensing, vol 55 n° 1 (January 2017)PermalinkPermalinkPermalinkRéseaux de neurones convolutifs pour la segmentation sémantique et l'apprentissage d'invariants de couleur / Damien Fourure (2017)PermalinkA review of volunteered geographic information quality assessment methods / Hansi Senaratne in International journal of geographical information science IJGIS, vol 31 n° 1-2 (January - February 2017)PermalinkPermalinkSingle Image Super-Resolution based on Neural Networks for text and face recognition / Clément Peyrard (2017)PermalinkSparsity, redundancy and robustness in artificial neural networks for learning and memory / Philippe Tigréat (2017)PermalinkSpatial co-location pattern mining of facility points-of-interest improved by network neighborhood and distance decay effects / Wenhao Yu in International journal of geographical information science IJGIS, vol 31 n° 1-2 (January - February 2017)PermalinkSVM et réseaux neuronaux convolutifs pour la classification de scènes urbaines / Amaury Zarzelli (2017)PermalinkThe use of logistic model tree (LMT) for pixel- and object-based classifications using high-resolution WorldView-2 imagery / Ismail Colkesen in Geocarto international, vol 32 n° 1 (January 2017)PermalinkPermalinkPermalinkVision stéréoscopique temps-réel pour la navigation autonome d'un robot en environnement dynamique / Maxime Derome (2017)PermalinkPermalink