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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)
[article]
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] CartologieRésumé : (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
in Cartography and Geographic Information Science > vol 49 n° 3 (May 2022) . - pp 252 - 270[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 032-2022031 RAB Revue Centre de documentation En réserve L003 Disponible Unsupervised multi-view CNN for salient view selection and 3D interest point detection / Ran Song in International journal of computer vision, vol 130 n° 5 (May 2022)
[article]
Titre : Unsupervised multi-view CNN for salient view selection and 3D interest point detection Type de document : Article/Communication Auteurs : Ran Song, Auteur ; Wei Zhang, Auteur ; Yitian Zhao, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 1210 - 1227 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] classification par réseau neuronal convolutif
[Termes IGN] détection d'objet
[Termes IGN] objet 3D
[Termes IGN] point d'intérêt
[Termes IGN] saillanceRésumé : (auteur) We present an unsupervised 3D deep learning framework based on a ubiquitously true proposition named by us view-object consistency as it states that a 3D object and its projected 2D views always belong to the same object class. To validate its effectiveness, we design a multi-view CNN instantiating it for salient view selection and interest point detection of 3D objects, which quintessentially cannot be handled by supervised learning due to the difficulty of collecting sufficient and consistent training data. Our unsupervised multi-view CNN, namely UMVCNN, branches off two channels which encode the knowledge within each 2D view and the 3D object respectively and also exploits both intra-view and inter-view knowledge of the object. It ends with a new loss layer which formulates the view-object consistency by impelling the two channels to generate consistent classification outcomes. The UMVCNN is then integrated with a global distinction adjustment scheme to incorporate global cues into salient view selection. We evaluate our method for salient view section both qualitatively and quantitatively, demonstrating its superiority over several state-of-the-art methods. In addition, we showcase that our method can be used to select salient views of 3D scenes containing multiple objects. We also develop a method based on the UMVCNN for 3D interest point detection and conduct comparative evaluations on a publicly available benchmark, which shows that the UMVCNN is amenable to different 3D shape understanding tasks. Numéro de notice : A2022-415 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007/s11263-022-01592-x Date de publication en ligne : 16/03/2022 En ligne : https://doi.org/10.1007/s11263-022-01592-x Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100771
in International journal of computer vision > vol 130 n° 5 (May 2022) . - pp 1210 - 1227[article]L’usage des cartes en temps de guerre / Olivier Razemon in Géomètre, n° 2202 (mai 2022)
[article]
Titre : L’usage des cartes en temps de guerre Type de document : Article/Communication Auteurs : Olivier Razemon, Auteur Année de publication : 2022 Article en page(s) : pp 18 - 20 Langues : Français (fre) Descripteur : [Vedettes matières IGN] Cartographie thématique
[Termes IGN] carte militaire
[Termes IGN] cartographie collaborative
[Termes IGN] cartographie étrangère
[Termes IGN] Google Maps
[Termes IGN] guerre
[Termes IGN] OpenStreetMap
[Termes IGN] Russie
[Termes IGN] temps réel
[Termes IGN] UkraineRésumé : (Auteur) Depuis la fin février, les cartes de l’Ukraine sont innombrables, accessibles, documentées, parfois interactives. Ces représentations confirment l’importance prise par les géants du numérique, surtout en temps de guerre. Numéro de notice : A2022-278 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtSansCL DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101038
in Géomètre > n° 2202 (mai 2022) . - pp 18 - 20[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 063-2022051 RAB Revue Centre de documentation En réserve L003 Disponible Weakly supervised semantic segmentation of airborne laser scanning point clouds / Yaping Lin in ISPRS Journal of photogrammetry and remote sensing, vol 187 (May 2022)
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Titre : Weakly supervised semantic segmentation of airborne laser scanning point clouds Type de document : Article/Communication Auteurs : Yaping Lin, Auteur ; M. George Vosselman, Auteur ; Michael Ying Yang, Auteur Année de publication : 2022 Article en page(s) : pp 79 - 100 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] apprentissage profond
[Termes IGN] chevauchement
[Termes IGN] classification dirigée
[Termes IGN] données étiquetées d'entrainement
[Termes IGN] données laser
[Termes IGN] données localisées 3D
[Termes IGN] hétérogénéité sémantique
[Termes IGN] segmentation sémantique
[Termes IGN] semis de pointsRésumé : (Auteur) While modern deep learning algorithms for semantic segmentation of airborne laser scanning (ALS) point clouds have achieved considerable success, the training process often requires a large number of labelled 3D points. Pointwise annotation of 3D point clouds, especially for large scale ALS datasets, is extremely time-consuming work. Weak supervision that only needs a few annotation efforts but can make networks achieve comparable performance is an alternative solution. Assigning a weak label to a subcloud, a group of points, is an efficient annotation strategy. With the supervision of subcloud labels, we first train a classification network that produces pseudo labels for the training data. Then the pseudo labels are taken as the input of a segmentation network which gives the final predictions on the testing data. As the quality of pseudo labels determines the performance of the segmentation network on testing data, we propose an overlap region loss and an elevation attention unit for the classification network to obtain more accurate pseudo labels. The overlap region loss that considers the nearby subcloud semantic information is introduced to enhance the awareness of the semantic heterogeneity within a subcloud. The elevation attention helps the classification network to encode more representative features for ALS point clouds. For the segmentation network, in order to effectively learn representative features from inaccurate pseudo labels, we adopt a supervised contrastive loss that uncovers the underlying correlations of class-specific features. Extensive experiments on three ALS datasets demonstrate the superior performance of our model to the baseline method (Wei et al., 2020). With the same amount of labelling efforts, for the ISPRS benchmark dataset, the Rotterdam dataset and the DFC2019 dataset, our method rises the overall accuracy by 0.062, 0.112 and 0.031, and the average F1 score by 0.09, 0.178 and 0.043 respectively. Our code is publicly available at ‘https://github.com/yaping222/Weak_ALS.git’. Numéro de notice : A2022-227 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2022.03.001 Date de publication en ligne : 11/03/2022 En ligne : https://doi.org/10.1016/j.isprsjprs.2022.03.001 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100197
in ISPRS Journal of photogrammetry and remote sensing > vol 187 (May 2022) . - pp 79 - 100[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2022051 SL Revue Centre de documentation Revues en salle Disponible 081-2022053 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2022052 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt Automated inventory of broadleaf tree plantations with UAS imagery / Aishwarya Chandrasekaran in Remote sensing, vol 14 n° 8 (April-2 2022)
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Titre : Automated inventory of broadleaf tree plantations with UAS imagery Type de document : Article/Communication Auteurs : Aishwarya Chandrasekaran, Auteur ; Guofan Shao, Auteur ; Songlin Fei, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 1931 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] feuillu
[Termes IGN] hauteur à la base du houppier
[Termes IGN] hauteur des arbres
[Termes IGN] image captée par drone
[Termes IGN] Indiana (Etats-Unis)
[Termes IGN] inventaire forestier (techniques et méthodes)
[Termes IGN] modèle numérique de surface de la canopée
[Termes IGN] orthophotoplan numérique
[Termes IGN] plantation forestière
[Termes IGN] R (langage)
[Termes IGN] semis de points
[Termes IGN] structure-from-motionRésumé : (auteur) With the increased availability of unmanned aerial systems (UAS) imagery, digitalized forest inventory has gained prominence in recent years. This paper presents a methodology for automated measurement of tree height and crown area in two broadleaf tree plantations of different species and ages using two different UAS platforms. Using structure from motion (SfM), we generated canopy height models (CHMs) for each broadleaf plantation in Indiana, USA. From the CHMs, we calculated individual tree parameters automatically through an open-source web tool developed using the Shiny R package and assessed the accuracy against field measurements. Our analysis shows higher tree measurement accuracy with the datasets derived from multi-rotor platform (M600) than with the fixed wing platform (Bramor). The results show that our automated method could identify individual trees (F-score > 90%) and tree biometrics (root mean square error Numéro de notice : A2022-351 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.3390/rs14081931 Date de publication en ligne : 16/04/2022 En ligne : https://doi.org/10.3390/rs14081931 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100539
in Remote sensing > vol 14 n° 8 (April-2 2022) . - n° 1931[article]Spectral-spatial classification method for hyperspectral images using stacked sparse autoencoder suitable in limited labelled samples situation / Seyyed Ali Ahmadi in Geocarto international, vol 37 n° 7 ([15/04/2022])PermalinkWood decay detection in Norway spruce forests based on airborne hyperspectral and ALS data / Michele Dalponte in Remote sensing, vol 14 n° 8 (April-2 2022)PermalinkAn exact statistical method for analyzing co-location on a street network and its computational implementation / Wataru Morioka in International journal of geographical information science IJGIS, vol 36 n° 4 (April 2022)PermalinkAssessment of land suitability potentials for winter wheat cultivation by using a multi criteria decision Support-Geographic information system (MCDS-GIS) approach in Al-Yarmouk Basin (Syria) / Safwan Mohammed in Geocarto international, vol 37 n° 6 ([01/04/2022])PermalinkClustering with implicit constraints: A novel approach to housing market segmentation / Xiaoqi Zhang in Transactions in GIS, vol 26 n° 2 (April 2022)PermalinkComparison of neural networks and k-nearest neighbors methods in forest stand variable estimation using airborne laser data / Andras Balazs in ISPRS Open Journal of Photogrammetry and Remote Sensing, vol 4 (April 2022)PermalinkDeep generative model for spatial–spectral unmixing with multiple endmember priors / Shuaikai Shi in IEEE Transactions on geoscience and remote sensing, vol 60 n° 4 (April 2022)PermalinkDeep learning for archaeological object detection on LiDAR: New evaluation measures and insights / Marco Fiorucci in Remote sensing, vol 14 n° 7 (April-1 2022)PermalinkDetecting individuals' spatial familiarity with urban environments using eye movement data / Hua Liao in Computers, Environment and Urban Systems, vol 93 (April 2022)PermalinkDetermination of building flood risk maps from LiDAR mobile mapping data / Yu Feng in Computers, Environment and Urban Systems, vol 93 (April 2022)PermalinkDiscovering co-location patterns in multivariate spatial flow data / Jiannan Cai in International journal of geographical information science IJGIS, vol 36 n° 4 (April 2022)PermalinkEnriching the metadata of map images: a deep learning approach with GIS-based data augmentation / Yingjie Hu in International journal of geographical information science IJGIS, vol 36 n° 4 (April 2022)PermalinkExploring scientific literature by textual and image content using DRIFT / Ximena Pocco in Computers and graphics, vol 103 (April 2022)PermalinkExploring the association between street built environment and street vitality using deep learning methods / Yunqin Li in Sustainable Cities and Society, vol 79 (April 2022)PermalinkA GAN-based approach toward architectural line drawing colorization prototyping / Qian (Chayn) Sun in The Visual Computer, vol 38 n° 4 (April 2022)PermalinkGeoRec: Geometry-enhanced semantic 3D reconstruction of RGB-D indoor scenes / Linxi Huan in ISPRS Journal of photogrammetry and remote sensing, vol 186 (April 2022)PermalinkA graph attention network for road marking classification from mobile LiDAR point clouds / Lina Fang in International journal of applied Earth observation and geoinformation, vol 108 (April 2022)PermalinkGraph learning based on signal smoothness representation for homogeneous and heterogeneous change detection / David Alejandro Jimenez-Sierra in IEEE Transactions on geoscience and remote sensing, vol 60 n° 4 (April 2022)PermalinkGraph neural network based model for multi-behavior session-based recommendation / Bo Yu in Geoinformatica, vol 26 n° 2 (April 2022)PermalinkHigh-performance adaptive texture streaming and rendering of large 3D cities / Alex Zhang in The Visual Computer, vol 38 n° 4 (April 2022)Permalink