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Automated conflation of digital elevation model with reference hydrographic lines / Timofey Samsonov in ISPRS International journal of geo-information, vol 9 n° 5 (May 2020)
[article]
Titre : Automated conflation of digital elevation model with reference hydrographic lines Type de document : Article/Communication Auteurs : Timofey Samsonov, Auteur Année de publication : 2020 Article en page(s) : 40 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] alignement
[Termes IGN] cartographie hydrographique
[Termes IGN] conflation
[Termes IGN] données localisées
[Termes IGN] données vectorielles
[Termes IGN] modèle numérique de surface
[Termes IGN] réseau de drainage
[Termes IGN] Triangulated Irregular Network
[Vedettes matières IGN] GénéralisationRésumé : (auteur) Combining misaligned spatial data from different sources complicates spatial analysis and creation of maps. Conflation is a process that solves the misalignment problem through spatial adjustment or attribute transfer between similar features in two datasets. Even though a combination of digital elevation model (DEM) and vector hydrographic lines is a common practice in spatial analysis and mapping, no method for automated conflation between these spatial data types has been developed so far. The problem of DEM and hydrography misalignment arises not only in map compilation, but also during the production of generalized datasets. There is a lack of automated solutions which can ensure that the drainage network represented in the surface of generalized DEM is spatially adjusted with independently generalized vector hydrography. We propose a new method that performs the conflation of DEM with linear hydrographic data and is embeddable into DEM generalization process. Given a set of reference hydrographic lines, our method automatically recognizes the most similar paths on DEM surface called counterpart streams. The elevation data extracted from DEM is then rubbersheeted locally using the links between counterpart streams and reference lines, and the conflated DEM is reconstructed from the rubbersheeted elevation data. The algorithm developed for extraction of counterpart streams ensures that the resulting set of lines comprises the network similar to the network of ordered reference lines. We also show how our approach can be seamlessly integrated into a TIN-based structural DEM generalization process with spatial adjustment to pre-generalized hydrographic lines as additional requirement. The combination of the GEBCO_2019 DEM and the Natural Earth 10M vector dataset is used to illustrate the effectiveness of DEM conflation both in map compilation and map generalization workflows. Resulting maps are geographically correct and are aesthetically more pleasing in comparison to a straightforward combination of misaligned DEM and hydrographic lines without conflation. Numéro de notice : A2020-297 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi9050334 Date de publication en ligne : 20/05/2020 En ligne : https://doi.org/10.3390/ijgi9050334 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95135
in ISPRS International journal of geo-information > vol 9 n° 5 (May 2020) . - 40 p.[article]Automatic extraction of road intersection points from USGS historical map series using deep convolutional neural networks / Mahmoud Saeedimoghaddam in International journal of geographical information science IJGIS, vol 34 n° 5 (May 2020)
[article]
Titre : Automatic extraction of road intersection points from USGS historical map series using deep convolutional neural networks Type de document : Article/Communication Auteurs : Mahmoud Saeedimoghaddam, Auteur ; Tomasz F. Stepinski, Auteur Année de publication : 2020 Article en page(s) : pp 947 - 968 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] carrefour
[Termes IGN] carte ancienne
[Termes IGN] carte numérisée
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] détection d'objet
[Termes IGN] données localisées
[Termes IGN] Etats-Unis
[Termes IGN] extraction du réseau routier
[Termes IGN] image RVB
[Termes IGN] numérisation automatique
[Termes IGN] représentation cartographique
[Termes IGN] système d'information géographique
[Termes IGN] vision par ordinateurRésumé : (auteur) Road intersection data have been used across a range of geospatial analyses. However, many datasets dating from before the advent of GIS are only available as historical printed maps. To be analyzed by GIS software, they need to be scanned and transformed into a usable (vector-based) format. Because the number of scanned historical maps is voluminous, automated methods of digitization and transformation are needed. Frequently, these processes are based on computer vision algorithms. However, the key challenges to this are (1) the low conversion accuracy for low quality and visually complex maps, and (2) the selection of optimal parameters. In this paper, we used a region-based deep convolutional neural network-based framework (RCNN) for object detection, in order to automatically identify road intersections in historical maps of several cities in the United States of America. We found that the RCNN approach is more accurate than traditional computer vision algorithms for double-line cartographic representation of the roads, though its accuracy does not surpass all traditional methods used for single-line symbols. The results suggest that the number of errors in the outputs is sensitive to complexity and blurriness of the maps, and to the number of distinct red-green-blue (RGB) combinations within them. Numéro de notice : A2020-205 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2019.1696968 Date de publication en ligne : 28/11/2019 En ligne : https://doi.org/10.1080/13658816.2019.1696968 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94882
in International journal of geographical information science IJGIS > vol 34 n° 5 (May 2020) . - pp 947 - 968[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 079-2020051 RAB Revue Centre de documentation En réserve L003 Disponible 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)
[article]
Titre : Comparing the roles of landmark visual salience and semantic salience in visual guidance during indoor wayfinding Type de document : Article/Communication Auteurs : Weihua Dong, Auteur ; Tong Qin, Auteur ; Hua Liao, Auteur Année de publication : 2020 Article en page(s) : pp 229 - 243 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] analyse visuelle
[Termes IGN] interprétation (psychologie)
[Termes IGN] oculométrie
[Termes IGN] point de repère
[Termes IGN] questionnaire
[Termes IGN] saillance
[Termes IGN] scène intérieure
[Termes IGN] segmentation sémantique
[Termes IGN] test statistique
[Termes IGN] vision
[Termes IGN] vision par ordinateur
[Vedettes matières IGN] GéovisualisationRésumé : (auteur) Landmark visual salience (characterized by features that contrast with their surroundings and visual peculiarities) and semantic salience (characterized by features with unusual or important meaning and content in the environment) are two important factors that affect an individual’s visual attention during wayfinding. However, empirical evidence regarding which factor dominates visual guidance during indoor wayfinding is rare, especially in real-world environments. In this study, we assumed that semantic salience dominates the guidance of visual attention, which means that semantic salience will correlate with participants’ fixations more significantly than visual salience. Notably, in previous studies, semantic salience was shown to guide visual attention in static images or familiar scenes in a laboratory environment. To validate this assumption, first, we collected the eye movement data of 22 participants as they found their way through a building. We then computed the landmark visual and semantic salience using computer vision models and questionnaires, respectively. Finally, we conducted correlation tests to verify our assumption. The results failed to validate our assumption and show that the role of salience in visual guidance in a real-world wayfinding process is different from the role of salience in perceiving static images or scenes in a laboratory. Visual salience dominates visual attention during indoor wayfinding, but the roles of salience in visual guidance are mixed across different landmark classes and tasks. The results provide new evidence for understanding how pedestrians visually interpret landmark information during real-world indoor wayfinding. Numéro de notice : A2020-169 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/15230406.2019.1697965 Date de publication en ligne : 18/12/2019 En ligne : https://doi.org/10.1080/15230406.2019.1697965 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94841
in Cartography and Geographic Information Science > vol 47 n° 3 (May 2020) . - pp 229 - 243[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 032-2020031 RAB Revue Centre de documentation En réserve L003 Disponible Evaluating the impact of visualization of risk upon emergency route-planning / Lisa Cheong in International journal of geographical information science IJGIS, vol 34 n° 5 (May 2020)
[article]
Titre : Evaluating the impact of visualization of risk upon emergency route-planning Type de document : Article/Communication Auteurs : Lisa Cheong, Auteur ; Christoph Kinkeldey, Auteur ; Ingrid Burfurd, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 1022 - 1050 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Cartographie thématique
[Termes IGN] analyse géovisuelle
[Termes IGN] calcul d'itinéraire
[Termes IGN] cartographie d'urgence
[Termes IGN] cartographie des risques
[Termes IGN] inondation
[Termes IGN] représentation cartographique
[Termes IGN] secours d'urgence
[Termes IGN] sémiologie graphique
[Termes IGN] symbole graphiqueRésumé : (auteur) This paper reports on a controlled experiment evaluating how different cartographic representations of risk affect participants’ performance on a complex spatial decision task: route planning. The specific experimental scenario used is oriented towards emergency route-planning during flood response. The experiment compared six common abstract and metaphorical graphical symbolizations of risk. The results indicate a pattern of less-preferred graphical symbolizations associated with slower responses and lower-risk route choices. One mechanism that might explain these observed relationships would be that more complex and effortful maps promote closer attention paid by participants and lower levels of risk taking. Such user considerations have important implications for the design of maps and mapping interfaces for emergency planning and response. The data also highlights the importance of the ‘right decision, wrong outcome problem’ inherent in decision-making under uncertainty: in individual instances, more risky decisions do not always lead to worse outcomes. Numéro de notice : A2020-206 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2019.1701677 Date de publication en ligne : 12/12/2019 En ligne : https://doi.org/10.1080/13658816.2019.1701677 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94885
in International journal of geographical information science IJGIS > vol 34 n° 5 (May 2020) . - pp 1022 - 1050[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 079-2020051 RAB Revue Centre de documentation En réserve L003 Disponible Exploring the potential of deep learning segmentation for mountain roads generalisation / Azelle Courtial in ISPRS International journal of geo-information, vol 9 n° 5 (May 2020)
[article]
Titre : Exploring the potential of deep learning segmentation for mountain roads generalisation Type de document : Article/Communication Auteurs : Azelle Courtial , Auteur ; Achraf El Ayedi, Auteur ; Guillaume Touya , Auteur ; Xiang Zhang, Auteur Année de publication : 2020 Projets : 1-Pas de projet / Article en page(s) : n° 338 ; 21 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] 1:25.000
[Termes IGN] 1:250.000
[Termes IGN] Alpes (France)
[Termes IGN] apprentissage profond
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] données routières
[Termes IGN] données vectorielles
[Termes IGN] généralisation automatique de données
[Termes IGN] montagne
[Termes IGN] route
[Termes IGN] segmentation
[Termes IGN] symbole graphique
[Termes IGN] virage
[Vedettes matières IGN] GénéralisationRésumé : (auteur) Among cartographic generalisation problems, the generalisation of sinuous bends in mountain roads has always been a popular one due to its difficulty. Recent research showed the potential of deep learning techniques to overcome some remaining research problems regarding the automation of cartographic generalisation. This paper explores this potential on the popular mountain road generalisation problem, which requires smoothing the road, enlarging the bend summits, and schematising the bend series by removing some of the bends. We modelled the mountain road generalisation as a deep learning problem by generating an image from input vector road data, and tried to generate it as an output of the model a new image of the generalised roads. Similarly to previous studies on building generalisation, we used a U-Net architecture to generate the generalised image from the ungeneralised image. The deep learning model was trained and evaluated on a dataset composed of roads in the Alps extracted from IGN (the French national mapping agency) maps at 1:250,000 (output) and 1:25,000 (input) scale. The results are encouraging as the output image looks like a generalised version of the roads and the accuracy of pixel segmentation is around 65%. The model learns how to smooth the output roads, and that it needs to displace and enlarge symbols but does not always correctly achieve these operations. This article shows the ability of deep learning to understand and manage the geographic information for generalisation, but also highlights challenges to come. Numéro de notice : A2020-295 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi9050338 Date de publication en ligne : 25/05/2020 En ligne : https://doi.org/10.3390/ijgi9050338 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95131
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