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The effect of map label language on the visual search of cartographic point symbols / Paweł Cybulski in Cartography and Geographic Information Science, vol 49 n° 3 (May 2022)
[article]
Titre : The effect of map label language on the visual search of cartographic point symbols Type de document : Article/Communication Auteurs : Paweł Cybulski, Auteur ; Vassilios Krassanakis, Auteur Année de publication : 2022 Article en page(s) : pp 189 - 204 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] détection de cible
[Termes IGN] étiquette
[Termes IGN] langage cartographique
[Termes IGN] langue
[Termes IGN] lecture de carte
[Termes IGN] oculométrie
[Termes IGN] sémiologie graphique
[Termes IGN] symbole graphique
[Termes IGN] visualisation cartographique
[Vedettes matières IGN] CartologieRésumé : (auteur) The present study aims to examine how the visual search for cartographic symbols is affected by the language of map labels. More specifically, we explore the influence of native language in the performance of a visual search map task which is referred to target point symbol detection. The main research hypothesis is that the relative position of the target symbols plays a significant role in the visual search process, although labels language impacts reaction time. In a controlled laboratory experiment with 38 participants and eye tracking technology, we used maps with labels in participants’ native language (Polish) and in Chinese, which participants could neither read nor write. We find that the detection of target symbols with Chinese labels is faster when the symbol’s location is peripheral. On the other hand, faster detection of target symbols with labels in participants’ native language favors central location. It turned out that having noticed the target symbol, participants fixated on the native language label. For Chinese labels, having seen the target symbol, participants did not fixate on the label. It also turned out that when participants searched for a target symbol located in the peripheral zone, more visual attention was in this zone. However, when the target symbol’s location was central, the participants’ visual attention focused mostly on the central zone. This confirms the significant role of the location of cartographic symbols in the visual search process. Numéro de notice : A2022- 292 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/15230406.2021.2007419 Date de publication en ligne : 16/12/2021 En ligne : https://doi.org/10.1080/15230406.2021.2007419 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100340
in Cartography and Geographic Information Science > vol 49 n° 3 (May 2022) . - pp 189 - 204[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 032-2022031 RAB Revue Centre de documentation En réserve L003 Disponible Deep mass redistribution prior to the 2010 Mw 8.8 Maule (Chile) Earthquake revealed by GRACE satellite gravity / Marie Bouih in Earth and planetary science letters, vol 584 (15 April 2022)
[article]
Titre : Deep mass redistribution prior to the 2010 Mw 8.8 Maule (Chile) Earthquake revealed by GRACE satellite gravity Type de document : Article/Communication Auteurs : Marie Bouih , Auteur ; Isabelle Panet , Auteur ; Dominique Remy, Auteur ; Laurent Longuevergne, Auteur ; Sylvain Bonvalot, Auteur Année de publication : 2022 Projets : Université de Paris / Clerici, Christine Conférence : EGU 2022, General Assembly 23/05/2022 27/05/2022 Vienne Autriche OA Abstracts only Article en page(s) : n° 117465 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie physique
[Termes IGN] champ de pesanteur terrestre
[Termes IGN] Chili
[Termes IGN] déformation de la croute terrestre
[Termes IGN] données GRACE
[Termes IGN] gradient de gravitation
[Termes IGN] jeu de données
[Termes IGN] levé gravimétrique
[Termes IGN] prévention des risques
[Termes IGN] risque naturel
[Termes IGN] séisme
[Termes IGN] signal
[Termes IGN] subduction
[Termes IGN] tectonique des plaquesRésumé : (auteur) Subduction zones megathrust faults constitute a considerable hazard as they produce most of the world's largest earthquakes. However, the role in megathrust earthquake generation exerted by deeper subduction processes remains poorly understood. Here, we analyze the 2003 – 2014 space-time variations of the Earth's gravity gradients derived from three datasets of GRACE geoid models over a large region surrounding the rupture zone of the Mw 8.8 Maule earthquake. In all these datasets, our analysis reveals a large-amplitude gravity gradient signal, progressively increasing in the three months before the earthquake, North of the epicentral area. We show that such signals are equivalent to a water storage decrease over 2 months and cannot be explained by hydrological sources nor artefacts, but rather find origin from mass redistributions within the solid Earth on the continental side of the subduction zone. These gravity gradient variations could be explained by an extensional deformation of the slab around 150-km depth along the Nazca Plate subduction direction, associated with large-scale fluid release. Furthermore, the lateral migration of the gravity signal towards the surface from a low coupling segment around North to the high coupling one in the South suggests that the Mw 8.8 earthquake may have originated from the propagation up to the trench of this deeper slab deformation. Our results highlight the importance of observations of the Earth's time-varying gravity field from satellites in order to probe slow mass redistributions in-depth major plate boundaries and provide new information on dynamic processes in the subduction system, essential to better understand the seismic cycle as a whole. Numéro de notice : A2022-280 Affiliation des auteurs : UMR IPGP-Géod+Ext (2020- ) Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.epsl.2022.117465 En ligne : https://doi.org/10.1016/j.epsl.2022.117465 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100288
in Earth and planetary science letters > vol 584 (15 April 2022) . - n° 117465[article]
[article]
Titre : Dater les documents cartographiques Type de document : Article/Communication Auteurs : Jean-Luc Arnaud, Auteur Année de publication : 2022 Article en page(s) : pp 1 - 18 Note générale : bibliographie Langues : Français (fre) Anglais (eng) Descripteur : [Vedettes matières IGN] Cartographie ancienne
[Termes IGN] cartographie militaire
[Termes IGN] datation
[Termes IGN] dépôt de la guerre
[Termes IGN] document cartographique
[Termes IGN] échelle cartographique
[Termes IGN] édition cartographique
[Termes IGN] histoire
[Termes IGN] précision
[Termes IGN] Service Géographique de l'Armée
[Termes IGN] utilisateurRésumé : (auteur) Cet article examine les modes de datation de la production cartographique française depuis la fin du XVIIIe siècle. Il est composé de sept chapitres thématiques qui envisagent la multiplicité des pratiques des éditeurs et montrent qu’en fonction de l’usage envisagé pour chaque document et de son niveau de précision, les enjeux portés par la datation prennent des formes différentes. Numéro de notice : A2022-294 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : sans En ligne : http://www.e-perimetron.org/Vol_17_1/Arnaud.pdf Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100346
in e-Perimetron > vol 17 n° 1 (avril 2022) . - pp 1 - 18[article]Deep learning for archaeological object detection on LiDAR: New evaluation measures and insights / Marco Fiorucci in Remote sensing, vol 14 n° 7 (April-1 2022)
[article]
Titre : Deep learning for archaeological object detection on LiDAR: New evaluation measures and insights Type de document : Article/Communication Auteurs : Marco Fiorucci, Auteur ; Wouter Baernd Verschoof-van der Vaart, Auteur ; Paolo Soleni, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 1694 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] apprentissage profond
[Termes IGN] classification barycentrique
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] classification pixellaire
[Termes IGN] détection d'objet
[Termes IGN] données d'entrainement (apprentissage automatique)
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] site archéologiqueRésumé : (auteur) Machine Learning-based workflows are being progressively used for the automatic detection of archaeological objects (intended as below-surface sites) in remote sensing data. Despite promising results in the detection phase, there is still a lack of a standard set of measures to evaluate the performance of object detection methods, since buried archaeological sites often have distinctive shapes that set them aside from other types of objects included in mainstream remote sensing datasets (e.g., Dataset of Object deTection in Aerial images, DOTA). Additionally, archaeological research relies heavily on geospatial information when validating the output of an object detection procedure, a type of information that is not normally considered in regular machine learning validation pipelines. This paper tackles these shortcomings by introducing two novel automatic evaluation measures, namely ‘centroid-based’ and ‘pixel-based’, designed to encode the salient aspects of the archaeologists’ thinking process. To test their usability, an experiment with different object detection deep neural networks was conducted on a LiDAR dataset. The experimental results show that these two automatic measures closely resemble the semi-automatic one currently used by archaeologists and therefore can be adopted as fully automatic evaluation measures in archaeological remote sensing detection. Adoption will facilitate cross-study comparisons and close collaboration between machine learning and archaeological researchers, which in turn will encourage the development of novel human-centred archaeological object detection tools. Numéro de notice : A2022-282 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.3390/rs14071694 En ligne : https://doi.org/10.3390/rs14071694 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100298
in Remote sensing > vol 14 n° 7 (April-1 2022) . - n° 1694[article]Detecting individuals' spatial familiarity with urban environments using eye movement data / Hua Liao in Computers, Environment and Urban Systems, vol 93 (April 2022)
[article]
Titre : Detecting individuals' spatial familiarity with urban environments using eye movement data Type de document : Article/Communication Auteurs : Hua Liao, Auteur ; Wendi Zhao, Auteur ; Changbo Zhang, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 101758 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] analyse visuelle
[Termes IGN] apprentissage automatique
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] navigation pédestre
[Termes IGN] oculométrie
[Termes IGN] service fondé sur la position
[Termes IGN] zone urbaine
[Vedettes matières IGN] GéovisualisationRésumé : (auteur) The spatial familiarity of environments is an important high-level user context for location-based services (LBS). Knowing users' familiarity level of environments is helpful for enabling context-aware LBS that can automatically adapt information services according to users' familiarity with the environment. Unlike state-of-the-art studies that used questionnaires, sketch maps, mobile phone positioning (GPS) data, and social media data to measure spatial familiarity, this study explored the potential of a new type of sensory data - eye movement data - to infer users' spatial familiarity of environments using a machine learning approach. We collected 38 participants' eye movement data when they were performing map-based navigation tasks in familiar and unfamiliar urban environments. We trained and cross-validated a random forest classifier to infer whether the users were familiar or unfamiliar with the environments (i.e., binary classification). By combining basic statistical features and fixation semantic features, we achieved a best accuracy of 81% in a 10-fold classification and 70% in the leave-one-task-out (LOTO) classification. We found that the pupil diameter, fixation dispersion, saccade duration, fixation count and duration on the map were the most important features for detecting users' spatial familiarity. Our results indicate that detecting users' spatial familiarity from eye tracking data is feasible in map-based navigation and only a few seconds (e.g., 5 s) of eye movement data is sufficient for such detection. These results could be used to develop context-aware LBS that adapt their services to users' familiarity with the environments. Numéro de notice : A2022-121 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1016/j.compenvurbsys.2022.101758 Date de publication en ligne : 21/01/2022 En ligne : https://doi.org/10.1016/j.compenvurbsys.2022.101758 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99663
in Computers, Environment and Urban Systems > vol 93 (April 2022) . - n° 101758[article]Determination of building flood risk maps from LiDAR mobile mapping data / Yu Feng in Computers, Environment and Urban Systems, vol 93 (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)PermalinkFlood mapping using multi-temporal Sentinel-1 SAR images: A case study—Inaouene watershed from Northeast of Morocco / Brahim Benzougagh in Iranian Journal of Science and Technology - Transactions of Civil Engineering, vol 46 n° 2 (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)PermalinkGraph neural network based model for multi-behavior session-based recommendation / Bo Yu in Geoinformatica, vol 26 n° 2 (April 2022)PermalinkHuman movement patterns of different racial-ethnic and economic groups in U.S. top 50 populated cities: What can social media tell us about isolation? / Meiliu Wu in Annals of GIS, vol 28 n° 2 (April 2022)PermalinkNatural disturbances risks in European boreal and temperate forests and their links to climate change : A review of modelling approaches / Joyce Machado Nunes Romeiro in Forest ecology and management, vol 509 (April-1 2022)PermalinkSpatial modeling of migration using GIS-based multi-criteria decision analysis: A case study of Iran / Naeim Mijani in Transactions in GIS, vol 26 n° 2 (April 2022)PermalinkAutomated 3D reconstruction of LoD2 and LoD1 models for All 10 million buildings of the Netherlands / Ravi Peters in Photogrammetric Engineering & Remote Sensing, PERS, vol 88 n° 3 (March 2022)PermalinkComparaison des images satellite et aériennes dans le domaine de la détection d’obstacles à la navigation aérienne et de leur mise à jour / Olivier de Joinville in XYZ, n° 170 (mars 2022)Permalink