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Swipe versus multiple view: a comprehensive analysis using eye-tracking to evaluate user interaction with web maps / Stanislav Popelka in Cartography and Geographic Information Science, vol 49 n° 3 (May 2022)  

Public [article]  inCartography and Geographic Information Science > vol 49 n° 3 (May 2022) . - pp 252 - 270 Titre : Swipe versus multiple view: a comprehensive analysis using eye-tracking to evaluate user interaction with web maps Type de document : Article/Communication Auteurs : Stanislav Popelka, Auteur ; Jaroslav Burian, Auteur ; Marketa Beitlova, Auteur Année de publication : 2022 Article en page(s) : pp 252 - 270 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] analyse géovisuelle [Termes IGN] ArcGIS online [Termes IGN] carte interactive [Termes IGN] cartographie par internet [Termes IGN] interactivité [Termes IGN] interface web [Termes IGN] oculométrie [Termes IGN] représentation cognitive [Termes IGN] utilisateur civil [Termes IGN] vision [Termes IGN] web mapping [Vedettes matières IGN] Cartologie Résumé : (auteur) The comparison of multiple maps is a common fundamental process used by geographers to explore the world. The most frequently applied interactive methods for the comparison of maps are multiple view and swipe. Swipe allows the user to interactively drag and overlap two different maps. Multiple view is based on the simultaneous side-by-side display of several maps. The current paper presents an analysis of the use of these two map comparison techniques in an Esri environment using an eye-tracking study which involved 25 participants. The participants completed two different tasks which compared land suitability using two or four maps. Based on an analysis of the recorded data, we compared the effectiveness of these methods through the accuracy of answers, the trial duration, and eye-tracking metrics of the individual compositional elements of the interactive maps. Cognitive processing was investigated through the analysis of dynamic areas of interest. This labor-intensive analysis yielded results which could be visualized using sequence charts. Based on these analyses, we concluded that the participants worked more effectively with multiple views, especially in comparing four maps. Working with swipe in the Esri environment is non-intuitive in comparisons of more than two maps. Many participants instead preferred simple toggling between layers instead of interactive swipe comparisons. However, when swipe was used to compare two maps, the method was more efficient, especially during cognitively demanding tasks. Numéro de notice : A2022-293 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/15230406.2021.2015721 Date de publication en ligne : 25/01/2022 En ligne : https://doi.org/10.1080/15230406.2021.2015721 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100343 [article]

Building footprint extraction in Yangon city from monocular optical satellite image using deep learning / Hein Thura Aung in Geocarto international, vol 37 n° 3 ([01/03/2022])  

Public [article]  inGeocarto international > vol 37 n° 3 [01/03/2022] . - pp 792 - 812 Titre : Building footprint extraction in Yangon city from monocular optical satellite image using deep learning Type de document : Article/Communication Auteurs : Hein Thura Aung, Auteur ; Sao Hone Pha, Auteur ; Wataru Takeuchi, Auteur Année de publication : 2022 Article en page(s) : pp 792 - 812 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage profond [Termes IGN] Birmanie [Termes IGN] détection du bâti [Termes IGN] empreinte [Termes IGN] image Geoeye [Termes IGN] image isolée [Termes IGN] réseau antagoniste génératif [Termes IGN] vision monoculaire Résumé : (auteur) In this research, building footprints in Yangon City, Myanmar are extracted only from monocular optical satellite image by using conditional generative adversarial network (CGAN). Both training dataset and validating dataset are created from GeoEYE image of Dagon Township in Yangon City. Eight training models are created according to the change of values in three training parameters; learning rate, β1 term of Adam, and number of filters in the first convolution layer of the generator and the discriminator. The images of the validating dataset are divided into four image groups; trees, buildings, mixed trees and buildings, and pagodas. The output images of eight trained models are transformed to the vector images and then evaluated by comparing with manually digitized polygons using completeness, correctness and F1 measure. According to the results, by using CGAN, building footprints can be extracted up to 71% of completeness, 81% of correctness and 69% of F1 score from only monocular optical satellite image. Numéro de notice : A2022-345 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2020.1740949 Date de publication en ligne : 20/03/2020 En ligne : https://doi.org/10.1080/10106049.2020.1740949 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100526 [article]

LiDAR-based method for analysing landmark visibility to pedestrians in cities: case study in Kraków, Poland / Krystian Pyka in International journal of geographical information science IJGIS, vol 36 n° 3 (March 2022)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 36 n° 3 (March 2022) . - pp 476 - 495 Titre : LiDAR-based method for analysing landmark visibility to pedestrians in cities: case study in Kraków, Poland Type de document : Article/Communication Auteurs : Krystian Pyka, Auteur ; Radoslaw Piskorski, Auteur ; Aleksandra Jasińska, Auteur Année de publication : 2022 Article en page(s) : pp 476 - 495 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] analyse visuelle [Termes IGN] canyon urbain [Termes IGN] Cracovie (Pologne) [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] modèle numérique de surface [Termes IGN] paysage urbain [Termes IGN] piéton [Termes IGN] point de repère [Termes IGN] scène urbaine [Termes IGN] semis de points [Termes IGN] visibilité (optique) [Termes IGN] vision Résumé : (auteur) We propose a method for analysing landmark visibility from a pedestrian’s perspective. A case study is performed in Kraków, a city with many architectural monuments, where airborne LiDAR is used to model both buildings and urban greenery. The proposed method involves preliminary and detailed stages. The preliminary stage entails an inverse analysis (I–Vis) that departs from the typical visibility analysis to enable the use of landmarks as observers instead of targets. I–Vis results in paths with high landmark visibility. The detailed stage involves the use of a virtual panorama (V-Pan) to determine the visual exposure of the landmarks. Landmarks considered visible by I–Vis are generally consistent with landmarks identified by V-Pan. Discrepancies occur when trees appear in the near field-of-view. In addition, the accuracy of the skyline length and visible landmark surface area is evaluated against ground observations. The obtained results show that V-Pan can evaluate landmark visibility with an accuracy of approximately 75%. The key contributions of the work to visibility analysis of urban landmarks are in the inverse viewshed strategy and evaluation of the visual exposure parameters on LiDAR virtual panoramas. Numéro de notice : A2022-206 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2021.2015600 Date de publication en ligne : 17/12/2021 En ligne : https://doi.org/10.1080/13658816.2021.2015600 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100021 [article]

Increasing territorial planning activities through viewshed analysis / Gheorghe-Gavrilă Hognogi in Geocarto international, vol 37 n° 2 ([15/01/2022])  

Public [article]  inGeocarto international > vol 37 n° 2 [15/01/2022] . - pp 627 - 637 Titre : Increasing territorial planning activities through viewshed analysis Type de document : Article/Communication Auteurs : Gheorghe-Gavrilă Hognogi, Auteur ; Ana-Maria Pop, Auteur ; Simona Mălăescu, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 627 - 637 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] aménagement du territoire [Termes IGN] analyse géovisuelle [Termes IGN] Carpates [Termes IGN] carte topographique [Termes IGN] logique floue [Termes IGN] point de visibilité [Termes IGN] Roumanie [Termes IGN] vision [Vedettes matières IGN] Géovisualisation Résumé : (auteur) Visibility analyses are employed in various fields, from landscape to archeology or territorial planning. Two case studies, of different elevation, from Romania were selected to be considered for setting up some observation points as lookout points. Fuzzy viewshed analysis was performed to evaluate the degree of visibility of certain landscape components and was also used as a tool for territorial planning. The main results of the research were some particular viewshed analysis area according to the dominant visibility directions. This methodology may be useful to local authorities, which are the only responsible bodies for authorizing, creating and setting up lookout points in a given space or for organizing certain planning activities. Numéro de notice : A2022-051 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2020.1730450 Date de publication en ligne : 27/02/2020 En ligne : https://doi.org/10.1080/10106049.2020.1730450 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99444 [article]

Unsupervised self-adaptive deep learning classification network based on the optic nerve microsaccade mechanism for unmanned aerial vehicle remote sensing image classification / Ming Cong in Geocarto international, vol 36 n° 18 ([01/10/2021])  

Public [article]  inGeocarto international > vol 36 n° 18 [01/10/2021] . - pp 2065 - 2084 Titre : Unsupervised self-adaptive deep learning classification network based on the optic nerve microsaccade mechanism for unmanned aerial vehicle remote sensing image classification Type de document : Article/Communication Auteurs : Ming Cong, Auteur ; Zhiye Wang, Auteur ; Yiting Tao, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 2065 - 2084 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse de groupement [Termes IGN] chromatopsie [Termes IGN] classification non dirigée [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] compréhension de l'image [Termes IGN] échantillonnage d'image [Termes IGN] filtrage numérique d'image [Termes IGN] image captée par drone [Termes IGN] vision [Termes IGN] vision par ordinateur Résumé : (auteur) Unmanned aerial vehicle remote sensing images need to be precisely and efficiently classified. However, complex ground scenes produced by ultra-high ground resolution, data uniqueness caused by multi-perspective observations, and need for manual labelling make it difficult for current popular deep learning networks to obtain reliable references from heterogeneous samples. To address these problems, this paper proposes an optic nerve microsaccade (ONMS) classification network, developed based on multiple dilated convolution. ONMS first applies a Laplacian of Gaussian filter to find typical features of ground objects and establishes class labels using adaptive clustering. Then, using an image pyramid, multi-scale image data are mapped to the class labels adaptively to generate homologous reliable samples. Finally, an end-to-end multi-scale neural network is applied for classification. Experimental results show that ONMS significantly reduces sample labelling costs while retaining high cognitive performance, classification accuracy, and noise resistance—indicating that it has significant application advantages. Numéro de notice : A2021-707 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1080/10106049.2019.1687593 Date de publication en ligne : 07/11/2019 En ligne : https://doi.org/10.1080/10106049.2019.1687593 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98602 [article]

Emotional habitat: mapping the global geographic distribution of human emotion with physical environmental factors using a species distribution model / Yizhuo Li in International journal of geographical information science IJGIS, vol 35 n° 2 (February 2021)  

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Deep convolutional neural networks for scene understanding and motion planning for self-driving vehicles / Abdelhak Loukkal (2021)  

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Deep-learning for 3D reconstruction / Fabio Tosi (2021)  

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Méthodes de partage d'informations visuelles et inertielles pour la localisation et la cartographie simultanées décentralisées multi-robots / Rodolphe Dubois (2021)  

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Visual exploration of historical image collections: An interactive approach through space and time / Evelyn Paiz-Reyes (2021)

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3D hand mesh reconstruction from a monocular RGB image / Hao Peng in The Visual Computer, vol 36 n° 10 - 12 (October 2020)  

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Comparing the roles of landmark visual salience and semantic salience in visual guidance during indoor wayfinding / Weihua Dong in Cartography and Geographic Information Science, vol 47 n° 3 (May 2020)  

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Refractive two-view reconstruction for underwater 3D vision / François Chadebecq in International journal of computer vision, vol 128 n° 5 (May 2020)  

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Multichannel Pulse-Coupled Neural Network-Based Hyperspectral Image Visualization / Puhong Duan in IEEE Transactions on geoscience and remote sensing, vol 58 n° 4 (April 2020)  

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Learning stereo reconstruction with deep neural networks / Stepan Tulyakov (2020)  

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