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Proxemic maps for immersive visualization / Zeinab Ghaemi in Cartography and Geographic Information Science, vol 49 n° 3 (May 2022)
[article]
Titre : Proxemic maps for immersive visualization Type de document : Article/Communication Auteurs : Zeinab Ghaemi, Auteur ; Ulrich Engelke, Auteur ; Barrett Ens, Auteur ; Bernhard Jenny, Auteur Année de publication : 2022 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] immersion
[Termes IGN] programme interactif
[Termes IGN] projection
[Vedettes matières IGN] GéovisualisationRésumé : (auteur) In human computer interaction, proxemics describes the ways that people use space to interact with other people or objects. We focus on proxemic maps, which are virtual maps in immersive environments that react to proxemic interaction. Proxemic maps take advantage of new opportunities brought about by immersive visualization, where virtual maps can be freely positioned in virtual or physical space and adapt themselves relative to the spatial position of the viewer. We discuss proxemic interactions that alter the content and type of maps, including changing scale, symbolization, type of visualization and geometry. We propose a novel transformation that changes the geometry of maps based on their proximity to users. Users move the map back and forth and the map transitions between ring, horizontal, vertical and cylindrical geometries. The ring geometry surrounds the user and aligns features on the map with features in the real world. We implemented the map transformation in virtual reality and conducted a user study to evaluate it. The results of the user study indicate that participants preferred the ring and horizontal geometries. The ring geometry is useful because it simplifies connecting virtual features on the map with real features in the landscape, while the horizontal geometry provides an overall view of the landscape. We further found that combination of different geometries helped the study participants to overcome the limitations of each geometry. Numéro de notice : A2022-142 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/15230406.2021.2013946 Date de publication en ligne : 24/01/2022 En ligne : https://doi.org/10.1080/15230406.2021.2013946 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99961
in Cartography and Geographic Information Science > vol 49 n° 3 (May 2022)[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 032-2022031 RAB Revue Centre de documentation En réserve L003 Disponible A review of maps in PhDs: Is your map worth a thousand words? / Serena Coetzee in Cartographic journal (the), vol 59 n° 2 (May 2022)
[article]
Titre : A review of maps in PhDs: Is your map worth a thousand words? Type de document : Article/Communication Auteurs : Serena Coetzee, Auteur ; Sanet Carow, Auteur ; Lourens Snyman, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 150 - 164 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Cartographie
[Termes IGN] conception cartographique
[Termes IGN] enseignement supérieur
[Termes IGN] lecture de carte
[Termes IGN] logiciel SIG
[Termes IGN] représentation cartographique
[Termes IGN] thèseRésumé : (auteur) Maps are useful for providing location context and for graphically presenting spatial relationships. They are often used in PhD dissertations to show the location of a study area or to present scientific results. These maps have to tell their story without the PhD candidate being present. We searched for maps in 575 PhD dissertations, and reviewed 192 maps in 65 of these: 38% were created by PhD candidates, 48% were inserted and 14% were adapted from other sources. Maps prepared by PhD candidates had more design shortcomings than other maps. Nevertheless, the number of problems with maps from other sources suggests that guidelines for including them in a dissertation could be useful. Our results suggest that PhD candidates use GIS software to design maps, but that there is room for improvement to guide users towards appropriate design choices. The results will help to plan support services for PhD candidates at universities. Numéro de notice : A2022-900 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/00087041.2021.2006980 Date de publication en ligne : 27/04/2022 En ligne : https://doi.org/10.1080/00087041.2021.2006980 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102282
in Cartographic journal (the) > vol 59 n° 2 (May 2022) . - pp 150 - 164[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 030-2022021 RAB Revue Centre de documentation En réserve L003 Disponible Revising cadastral data on land boundaries using deep learning in image-based mapping / Bujar Fetai in ISPRS International journal of geo-information, vol 11 n° 5 (May 2022)
[article]
Titre : Revising cadastral data on land boundaries using deep learning in image-based mapping Type de document : Article/Communication Auteurs : Bujar Fetai, Auteur ; Dejan Grigillo, Auteur ; Anka Lisec, Auteur Année de publication : 2022 Article en page(s) : n° 298 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage profond
[Termes IGN] cadastre étranger
[Termes IGN] cartographie cadastrale
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] détection de contours
[Termes IGN] données cadastrales
[Termes IGN] limite cadastrale
[Termes IGN] point d'appui
[Termes IGN] SlovénieRésumé : (auteur) One of the main concerns of land administration in developed countries is to keep the cadastral system up to date. The goal of this research was to develop an approach to detect visible land boundaries and revise existing cadastral data using deep learning. The convolutional neural network (CNN), based on a modified architecture, was trained using the Berkeley segmentation data set 500 (BSDS500) available online. This dataset is known for edge and boundary detection. The model was tested in two rural areas in Slovenia. The results were evaluated using recall, precision, and the F1 score—as a more appropriate method for unbalanced classes. In terms of detection quality, balanced recall and precision resulted in F1 scores of 0.60 and 0.54 for Ponova vas and Odranci, respectively. With lower recall (completeness), the model was able to predict the boundaries with a precision (correctness) of 0.71 and 0.61. When the cadastral data were revised, the low values were interpreted to mean that the lower the recall, the greater the need to update the existing cadastral data. In the case of Ponova vas, the recall value was less than 0.1, which means that the boundaries did not overlap. In Odranci, 21% of the predicted and cadastral boundaries overlapped. Since the direction of the lines was not a problem, the low recall value (0.21) was mainly due to overly fragmented plots. Overall, the automatic methods are faster (once the model is trained) but less accurate than the manual methods. For a rapid revision of existing cadastral boundaries, an automatic approach is certainly desirable for many national mapping and cadastral agencies, especially in developed countries. Numéro de notice : A2022-357 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi11050298 Date de publication en ligne : 04/05/2022 En ligne : https://doi.org/10.3390/ijgi11050298 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100562
in ISPRS International journal of geo-information > vol 11 n° 5 (May 2022) . - n° 298[article]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]Exemplaires(1)
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
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