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Mountain summit detection with Deep Learning: evaluation and comparison with heuristic methods / Rocio Nahime Torres in Applied geomatics, vol 12 n° 2 (June 2020)
[article]
Titre : Mountain summit detection with Deep Learning: evaluation and comparison with heuristic methods Type de document : Article/Communication Auteurs : Rocio Nahime Torres, Auteur Année de publication : 2020 Article en page(s) : pp 225 – 246 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] apprentissage profond
[Termes IGN] base de données altimétriques
[Termes IGN] classification floue
[Termes IGN] collecte de données
[Termes IGN] données localisées des bénévoles
[Termes IGN] figuré du terrain
[Termes IGN] méthode heuristique
[Termes IGN] modèle numérique de surface
[Termes IGN] montagne
[Termes IGN] OpenStreetMap
[Termes IGN] sommet (relief)
[Termes IGN] système d'information géographiqueRésumé : (auteur) Landform detection and analysis from Digital Elevation Models (DEM) of the Earth has been boosted by the availability of high-quality public data sets. Current landform identification methods apply heuristic algorithms based on predefined landform features, fine tuned with parameters that may depend on the region of interest. In this paper, we investigate the use of Deep Learning (DL) models to identify mountain summits based on features learned from data examples. We train DL models with the coordinates of known summits found in public databases and apply the trained models to DEM data obtaining as output the coordinates of candidate summits. We introduce two formulations of summit recognition (as a classification or a segmentation task), describe the respective DL models, compare them with heuristic methods quantitatively, illustrate qualitatively their performances, and discuss the challenges of training DL methods for landform recognition with highly unbalanced and noisy data sets. Numéro de notice : A2020-560 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s12518-019-00295-2 Date de publication en ligne : 24/12/2019 En ligne : https://doi.org/10.1007/s12518-019-00295-2 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95870
in Applied geomatics > vol 12 n° 2 (June 2020) . - pp 225 – 246[article]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
in ISPRS International journal of geo-information > vol 9 n° 5 (May 2020) . - n° 338 ; 21 p.[article]Geological map generalization driven by size constraints / Azimjon Sayidov in ISPRS International journal of geo-information, vol 9 n° 4 (April 2020)
[article]
Titre : Geological map generalization driven by size constraints Type de document : Article/Communication Auteurs : Azimjon Sayidov, Auteur ; Meysam Aliakbarian, Auteur ; Robert Weibel, Auteur Année de publication : 2020 Article en page(s) : 29 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] algorithme de généralisation
[Termes IGN] carte géologique
[Termes IGN] données environnementales
[Termes IGN] généralisation automatique de données
[Termes IGN] opérateur de généralisation
[Termes IGN] programmation par contraintes
[Termes IGN] prospection minérale
[Termes IGN] taille (variable visuelle)
[Vedettes matières IGN] GénéralisationRésumé : (auteur) Geological maps are an important information source used in the support of activities relating to mining, earth resources, hazards, and environmental studies. Owing to the complexity of this particular map type, the process of geological map generalization has not been comprehensively addressed, and thus a complete automated system for geological map generalization is not yet available. In particular, while in other areas of map generalization constraint-based techniques have become the prevailing approach in the past two decades, generalization methods for geological maps have rarely adopted this approach. This paper seeks to fill this gap by presenting a methodology for the automation of geological map generalization that builds on size constraints (i.e., constraints that deal with the minimum area and distance relations in individual or pairs of map features). The methodology starts by modeling relevant size constraints and then uses a workflow consisting of generalization operators that respond to violations of size constraints (elimination/selection, enlargement, aggregation, and displacement) as well as algorithms to implement these operators. We show that the automation of geological map generalization is possible using constraint-based modeling, leading to improved process control compared to current approaches. However, we also show the limitations of an approach that is solely based on size constraints and identify extensions for a more complete workflow. Numéro de notice : A2020-261 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi9040284 Date de publication en ligne : 24/04/2020 En ligne : https://doi.org/10.3390/ijgi9040284 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95021
in ISPRS International journal of geo-information > vol 9 n° 4 (April 2020) . - 29 p.[article]Variable DEM generalization using local entropy for terrain representation through scale / Paulo Raposo in International journal of cartography, Vol 6 n° 1 (March 2020)
[article]
Titre : Variable DEM generalization using local entropy for terrain representation through scale Type de document : Article/Communication Auteurs : Paulo Raposo, Auteur Année de publication : 2020 Article en page(s) : pp 99 - 120 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] courbe de niveau
[Termes IGN] entropie
[Termes IGN] filtre passe-bas
[Termes IGN] généralisation cartographique
[Termes IGN] lissage de valeur
[Termes IGN] modèle numérique de surface
[Termes IGN] représentation multiple
[Termes IGN] voisinage (relation topologique)
[Vedettes matières IGN] GénéralisationRésumé : (auteur) An automated method of variable digital elevation model (DEM) smoothing is presented. Using variably sized kernels to perform filtering, the method is driven by the entropy of local z-values in the DEM, i.e. the amount of information necessary to convey the elevation variety in the neighborhood of each pixel. This paper presents the method in service of low-pass filtering in order to smooth the raster, though other neighborhood-based filters could be implemented as well. When used in smoothing, the method successfully retains detail in areas of higher relief variation and suppresses it in areas of lower variation, thereby retaining more salient features like ridges, peaks, or incised valleys, while diminishing flatter ones. Varying the neighborhood size with which entropy calculations are made allows for filtering through continuous map scale, enabling multi-scale representation. The method also includes a simple correction for smoothed pixels such that their z-value range reflects that of the input DEM, thereby ensuring that subsequent products such as generated contour lines remain within correct ranges. Several illustrations are given of the method's results. Numéro de notice : A2020-072 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/23729333.2019.1687973 Date de publication en ligne : 16/12/2019 En ligne : https://doi.org/10.1080/23729333.2019.1687973 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94636
in International journal of cartography > Vol 6 n° 1 (March 2020) . - pp 99 - 120[article]Bertin’s graphic variables and online map makers: an empirical study of maps produced by prosumers and cartographers / Natalia Ipatow in Cartographica, vol 54 n° 4 (Winter 2019)PermalinkRobust multisource remote sensing image registration method based on scene shape similarity / Ming Hao in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 10 (October 2019)PermalinkEmpirical studies on the visual perception of spatial patterns in choropleth maps / Jochen Schiewe in KN, Journal of Cartography and Geographic Information, vol 69 n° 3 (September 2019)PermalinkVisual clutter reduction in zoomable proportional point symbol maps / Tomasz Opach in Cartography and Geographic Information Science, Vol 46 n° 4 (July 2019)PermalinkCartographic symbol design considerations for the space–time cube / Christopher League in Cartographic journal (the), Vol 56 n° 2 (May 2019)PermalinkBertin’s matrix concepts reconsidered: transformations of semantics and semiotics to support geovisualization use / Francis Harvey in Cartography and Geographic Information Science, vol 46 n° 2 (March 2019)PermalinkComparing finite and infinitesimal map distortion measures / Krisztian Kerkovits in International journal of cartography, vol 5 n° 1 (March 2019)PermalinkExpanding the Sémiologie Graphique for contemporary cartography, some ideas from visual semiotics, art history and design / Laurent Jégou in Cartography and Geographic Information Science, vol 46 n° 2 (March 2019)Permalink(re)Considering Bertin in the age of big data and visual analytics / Alan M. MacEachren in Cartography and Geographic Information Science, vol 46 n° 2 (March 2019)PermalinkCartographic design and the space–time cube / Irma Kveladze in Cartographic journal (the), Vol 56 n° 1 (February 2019)Permalink