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Comparing pedestrians’ gaze behavior in desktop and in real environments / Weihua Dong in Cartography and Geographic Information Science, Vol 47 n° 5 (September 2020)
[article]
Titre : Comparing pedestrians’ gaze behavior in desktop and in real environments Type de document : Article/Communication Auteurs : Weihua Dong, Auteur ; Hua Liao, Auteur ; Bing Liu, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 432 - 451 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] analyse comparative
[Termes IGN] analyse visuelle
[Termes IGN] comportement
[Termes IGN] espace urbain
[Termes IGN] lecture de carte
[Termes IGN] monde virtuel
[Termes IGN] navigation pédestre
[Termes IGN] oculométrie
[Termes IGN] piéton
[Termes IGN] test statistique
[Termes IGN] travail
[Termes IGN] vision par ordinateur
[Vedettes matières IGN] GéovisualisationRésumé : (auteur) This research is motivated by the widespread use of desktop environments in the lab and by the recent trend of conducting real-world eye-tracking experiments to investigate pedestrian navigation. Despite the existing significant differences between the real world and the desktop environments, how pedestrians’ visual behavior in real environments differs from that in desktop environments is still not well understood. Here, we report a study that recorded eye movements for a total of 82 participants while they were performing five common navigation tasks in an unfamiliar urban environment (N = 39) and in a desktop environment (N = 43). By analyzing where the participants allocated their visual attention, what objects they fixated on, and how they transferred their visual attention among objects during navigation, we found similarities and significant differences in the general fixation indicators, spatial fixation distributions and attention to the objects of interest. The results contribute to the ongoing debate over the validity of using desktop environments to investigate pedestrian navigation by providing insights into how pedestrians allocate their attention to visual stimuli to accomplish navigation tasks in the two environments. Numéro de notice : A2020-488 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/15230406.2020.176251 Date de publication en ligne : 29/05/2020 En ligne : https://doi.org/10.1080/15230406.2020.1762513 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95658
in Cartography and Geographic Information Science > Vol 47 n° 5 (September 2020) . - pp 432 - 451[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 032-2020051 RAB Revue Centre de documentation En réserve L003 Disponible Crater detection and registration of planetary images through marked point processes, multiscale decomposition, and region-based analysis / David Solarna in IEEE Transactions on geoscience and remote sensing, vol 58 n° 9 (September 2020)
[article]
Titre : Crater detection and registration of planetary images through marked point processes, multiscale decomposition, and region-based analysis Type de document : Article/Communication Auteurs : David Solarna, Auteur ; Alberto Gotelli, Auteur ; Jacqueline Le Moigne, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 6039 - 6058 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] cratère
[Termes IGN] détection de contours
[Termes IGN] distance de Hausdorff
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] image multitemporelle
[Termes IGN] image thermique
[Termes IGN] Mars (planète)
[Termes IGN] ondelette
[Termes IGN] processus ponctuel marqué
[Termes IGN] séparateur à vaste marge
[Termes IGN] transformation de Hough
[Termes IGN] zone d'intérêtRésumé : (auteur) Because of the large variety of planetary sensors and spacecraft already collecting data and with many new and improved sensors being planned for future missions, planetary science needs to integrate numerous multimodal image sources, and, as a consequence, accurate and robust registration algorithms are required. In this article, we develop a new framework for crater detection based on marked point processes (MPPs) that can be used for planetary image registration. MPPs were found to be effective for various object detection tasks in Earth observation, and a new MPP model is proposed here for detecting craters in planetary data. The resulting spatial features are exploited for registration, together with fitness functions based on the MPP energy, on the mean directed Hausdorff distance, and on the mutual information. Two different methods—one based on birth–death processes and region-of-interest analysis and the other based on graph cuts and decimated wavelets—are developed within the proposed framework. Experiments with a large set of images, including 13 thermal infrared and visible images of the Mars surface, 20 semisimulated multitemporal pairs of images of the Mars surface, and a real multitemporal image pair of the Lunar surface, demonstrate the effectiveness of the proposed framework in terms of crater detection performance as well as for subpixel registration accuracy. Numéro de notice : A2020-526 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.2970908 Date de publication en ligne : 18/03/2020 En ligne : https://doi.org/10.1109/TGRS.2020.2970908 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95704
in IEEE Transactions on geoscience and remote sensing > vol 58 n° 9 (September 2020) . - pp 6039 - 6058[article]CSVM architectures for pixel-wise object detection in high-resolution remote sensing images / Youyou Li in IEEE Transactions on geoscience and remote sensing, vol 58 n° 9 (September 2020)
[article]
Titre : CSVM architectures for pixel-wise object detection in high-resolution remote sensing images Type de document : Article/Communication Auteurs : Youyou Li, Auteur ; Farid Melgani, Auteur ; Binbin He, Auteur Année de publication : 2020 Article en page(s) : pp 6059 - 6070 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] détection d'objet
[Termes IGN] données d'entrainement (apprentissage automatique)
[Termes IGN] image captée par drone
[Termes IGN] processeur graphiqueRésumé : (auteur) Detecting objects becomes an increasingly important task in very high resolution (VHR) remote sensing imagery analysis. With the development of GPU-computing capability, a growing number of deep convolutional neural networks (CNNs) have been designed to address the object detection challenge. However, compared with CPU, GPU is much more costly. Therefore, GPU-based methods are less attractive in practical applications. In this article, we propose a CPU-based method that is based on convolutional support vector machines (CSVMs) to address the object detection challenge in VHR images. Experiments are conducted on three VHR and two unmanned aerial vehicle (UAV) data sets with very limited training data. Results show that the proposed CSVM achieves competitive performance compared to U-Net which is an efficient CNN-based model designed for small training data sets. Numéro de notice : A2020-527 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.2972289 Date de publication en ligne : 02/03/2020 En ligne : https://doi.org/10.1109/TGRS.2020.2972289 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95705
in IEEE Transactions on geoscience and remote sensing > vol 58 n° 9 (September 2020) . - pp 6059 - 6070[article]Geovisualization and harmonic analysis for the exploratory search of localized cyclic recurrences in spatio-temporal event data / Jacques Gautier in Geomatica, vol 74 n° 3 (September 2020)
[article]
Titre : Geovisualization and harmonic analysis for the exploratory search of localized cyclic recurrences in spatio-temporal event data Type de document : Article/Communication Auteurs : Jacques Gautier , Auteur ; Paule-Annick Davoine, Auteur ; Claire Cunty, Auteur Année de publication : 2020 Projets : 1-Pas de projet / Article en page(s) : pp 131 - 153 Note générale : bibliographie
This research was funded by the Region Auvergne-Rhône-Alpes.Langues : Anglais (eng) Descripteur : [Termes IGN] analyse géovisuelle
[Termes IGN] analyse harmonique
[Termes IGN] base de données spatiotemporelles
[Termes IGN] distribution spatiale
[Termes IGN] données spatiotemporelles
[Termes IGN] événement
[Termes IGN] exploration de données géographiques
[Vedettes matières IGN] GéovisualisationMots-clés libres : GrAPHiST Résumé : (auteur) Many geovisualization environments integrate graphical representations of time. Some of them include representation of both linear and cyclic aspects of time, providing an exploratory analysis of spatio-temporal data through several temporal cyclic scales. However, few of them provide an exploratory analysis of localized cyclic recurrences in spatio-temporal data. Ad hoc temporal diagrams, representing both linear and cyclic aspects of time, provide a visual search for cyclic recurrences in temporal data when the possibility is left to the user to perform a gradual modification of the represented cyclic scale’s duration. The combination of these graphic representations of time, with cartographic representations, displaying the spatial distribution of such cyclic recurrences, could provide an exploratory analysis of localized cyclic recurrences in spatio-temporal data. Mathematical tools coming from other scientific fields, such as the harmonic analysis, offer another way to identify cyclic behaviors in temporal data. Combining the visual approach offered by specifically designed geovisualization environments, with a harmonic analysis that suggests searching paths to the user during its exploratory analysis, can then improve the visual search for localized cyclic recurrences. We propose a geovisualization environment, which combines, on one hand, a visual analysis of localized cyclic recurrences in spatio-temporal data, using ad hoc temporal diagrams, cartographic representations, and specific semiologic rules, and on the other hand, mathematical tools, such as harmonic analysis and spatial clustering, that provide searching paths to the user for its visual analysis. This approach is supported by a geovisualization environment, GrAPHiST, which provides an exploratory analysis of spatio-temporal event data. Numéro de notice : A2020-821 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1139/geomat-2020-0004 Date de publication en ligne : 03/08/2020 En ligne : https://doi.org/10.1139/geomat-2020-0004 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97245
in Geomatica > vol 74 n° 3 (September 2020) . - pp 131 - 153[article]Heliport detection using artificial neural networks / Emre Baseski in Photogrammetric Engineering & Remote Sensing, PERS, vol 86 n° 9 (September 2020)
[article]
Titre : Heliport detection using artificial neural networks Type de document : Article/Communication Auteurs : Emre Baseski, Auteur Année de publication : 2020 Article en page(s) : pp 541-546 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] analyse comparative
[Termes IGN] apprentissage profond
[Termes IGN] détection d'objet
[Termes IGN] hélicoptère
[Termes IGN] image à haute résolution
[Termes IGN] réseau neuronal artificiel
[Termes IGN] zone militaireRésumé : (Auteur) Automatic image exploitation is a critical technology for quick content analysis of high-resolution remote sensing images. The presence of a heliport on an image usually implies an important facility, such as military facilities. Therefore, detection of heliports can reveal critical information about the content of an image. In this article, two learning-based algorithms are presented that make use of artificial neural networks to detect H-shaped, light-colored heliports. The first algorithm is based on shape analysis of the heliport candidate segments using classical artificial neural networks. The second algorithm uses deep-learning techniques. While deep learning can solve difficult problems successfully, classical-learning approaches can be tuned easily to obtain fast and reasonable results. Therefore, although the main objective of this article is heliport detection, it also compares a deep-learning based approach with a classical learning-based approach and discusses advantages and disadvantages of both techniques. Numéro de notice : A2020-439 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.86.9.541 Date de publication en ligne : 01/09/2020 En ligne : https://doi.org/10.14358/PERS.86.9.541 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95929
in Photogrammetric Engineering & Remote Sensing, PERS > vol 86 n° 9 (September 2020) . - pp 541-546[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 105-2020091 SL Revue Centre de documentation Revues en salle Disponible A lightweight ensemble spatiotemporal interpolation model for geospatial data / Shifen Cheng in International journal of geographical information science IJGIS, vol 34 n° 9 (September 2020)PermalinkMapping croplands of Europe, Middle East, Russia, and Central Asia using Landsat, Random Forest, and Google Earth Engine / Aparna R. Phalke in ISPRS Journal of photogrammetry and remote sensing, vol 167 (September 2020)PermalinkMultiscale supervised kernel dictionary learning for SAR target recognition / Lei Tao in IEEE Transactions on geoscience and remote sensing, vol 58 n° 9 (September 2020)PermalinkNEAT approach for testing and validation of geospatial network agent-based model processes: case study of influenza spread / Taylor Anderson in International journal of geographical information science IJGIS, vol 34 n° 9 (September 2020)PermalinkA novel deep learning instance segmentation model for automated marine oil spill detection / Shamsudeen Temitope Yekeen in ISPRS Journal of photogrammetry and remote sensing, vol 167 (September 2020)PermalinkA novel deep network and aggregation model for saliency detection / Ye Liang in The Visual Computer, vol 36 n° 9 (September 2020)PermalinkSemi-automatic building extraction from WorldView-2 imagery using taguchi optimization / Hasan Tonbul in Photogrammetric Engineering & Remote Sensing, PERS, vol 86 n° 9 (September 2020)PermalinkUsing OpenStreetMap data and machine learning to generate socio-economic indicators / Daniel Feldmeyer in ISPRS International journal of geo-information, vol 9 n° 9 (September 2020)PermalinkVehicle detection of multi-source remote sensing data using active fine-tuning network / Xin Wu in ISPRS Journal of photogrammetry and remote sensing, vol 167 (September 2020)PermalinkWater level prediction from social media images with a multi-task ranking approach / P. Chaudhary in ISPRS Journal of photogrammetry and remote sensing, vol 167 (September 2020)PermalinkX-ModalNet: A semi-supervised deep cross-modal network for classification of remote sensing data / Danfeng Hong in ISPRS Journal of photogrammetry and remote sensing, vol 167 (September 2020)PermalinkA regression model of spatial accuracy prediction for Openstreetmap buildings / Ibrahim Maidaneh Abdi in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-4-2020 (August 2020)PermalinkSemCity Toulouse: a benchmark for building instance segmentation in satellite images / Ribana Roscher in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-5-2020 (August 2020)PermalinkBreaking the eyes: how do users get started with a coordinated and multiple view geovisualization tool? / Izabela Golebiowska in Cartographic journal (the), Vol 57 n° 3 (August 2020)PermalinkDetecting abandoned farmland using harmonic analysis and machine learning / Heeyeun Yoon in ISPRS Journal of photogrammetry and remote sensing, vol 166 (August 2020)PermalinkExploration of OpenStreetMap missing built-up areas using twitter hierarchical clustering and deep learning in Mozambique / Hao Li in ISPRS Journal of photogrammetry and remote sensing, vol 166 (August 2020)PermalinkExtraction of built-up areas from Landsat-8 OLI data based on spectral-textural information and feature selection using support vector machine method / Vijendra Singh Bramhe in Geocarto international, vol 35 n° 10 ([01/08/2020])PermalinkExtraction of urban built-up areas from nighttime lights using artificial neural network / Tingting Xu in Geocarto international, vol 35 n° 10 ([01/08/2020])PermalinkIncorporating behavior into animal movement modeling: a constrained agent-based model for estimating visit probabilities in space-time prisms / Rebecca W. Loraamm in International journal of geographical information science IJGIS, vol 34 n° 8 (August 2020)PermalinkLeveraging photogrammetric mesh models for aerial-ground feature point matching toward integrated 3D reconstruction / Qing Zhu in ISPRS Journal of photogrammetry and remote sensing, vol 166 (August 2020)Permalink