Centre dedocumentation
scientifique

Elevation models for reproducible evaluation of terrain representation / Patrick Kennelly in Cartography and Geographic Information Science, vol 48 n° 1 (January 2021)  

Public [article]  inCartography and Geographic Information Science > vol 48 n° 1 (January 2021) . - pp 63 - 77 Titre : Elevation models for reproducible evaluation of terrain representation Type de document : Article/Communication Auteurs : Patrick Kennelly, Auteur ; Tom Patterson, Auteur ; Bernhard Jenny, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 63 - 77 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie numérique [Termes descripteurs IGN] altitude [Termes descripteurs IGN] données multiéchelles [Termes descripteurs IGN] figuré du terrain [Termes descripteurs IGN] modèle numérique de surface [Termes descripteurs IGN] réalité de terrain [Termes descripteurs IGN] relief [Termes descripteurs IGN] représentation du relief [Termes descripteurs IGN] reproductibilité [Termes descripteurs IGN] visualisation de données Résumé : (auteur) This paper proposes elevation models to promote, evaluate, and compare various terrain representation techniques. Our goal is to increase the reproducibility of terrain rendering algorithms and techniques across different scales and landscapes. We introduce elevation models of varying terrain types, available to the user at no cost, with minimal common data imperfections such as missing data values, resampling artifacts, and seams. Three multiscale elevation models are available, each consisting of a set of elevation grids, centered on the same geographic location, with increasing cell sizes and spatial extents. We also propose a collection of single-scale elevation models of archetypal landforms including folded ridges, a braided riverbed, active and stabilized sand dunes, and a volcanic caldera. An inventory of 78 publications with a total of 155 renderings illustrating terrain visualization techniques guided the selection of landform types in the elevation models. The benefits of using the proposed elevation models include straightforward comparison of terrain representation methods across different publications and better documentation of the source data, which increases the reproducibility of terrain representations. Numéro de notice : A2021-719 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/15230406.2020.1830856 date de publication en ligne : 04/11/2020 En ligne : https://doi.org/10.1080/15230406.2020.1830856 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96459 [article]

Application of various strategies and methodologies for landslide susceptibility maps on a basin scale: the case study of Val Tartano, Italy / Vasil Yordanov in Applied geomatics, vol 12 n° 4 (December 2020)  

Public [article]  inApplied geomatics > vol 12 n° 4 (December 2020) . - 23 p. Titre : Application of various strategies and methodologies for landslide susceptibility maps on a basin scale: the case study of Val Tartano, Italy Type de document : Article/Communication Auteurs : Vasil Yordanov, Auteur ; Maria Antonia Brovelli, Auteur Année de publication : 2020 Article en page(s) : 23 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] analyse de sensibilité [Termes descripteurs IGN] cartographie des risques [Termes descripteurs IGN] cartographie géomorphologique [Termes descripteurs IGN] classification par forêts aléatoires [Termes descripteurs IGN] effondrement de terrain [Termes descripteurs IGN] figuré linéaire [Termes descripteurs IGN] indice de risque [Termes descripteurs IGN] inventaire [Termes descripteurs IGN] Lombardie [Termes descripteurs IGN] modèle numérique de surface [Termes descripteurs IGN] modèle numérique de terrain [Termes descripteurs IGN] modèle statistique [Termes descripteurs IGN] régression logistique Résumé : (auteur) Landslide susceptibility mapping is a crucial initial step in risk mitigation strategies. Landslide hazards are widely spread all over the world and, as such, mapping the relevant susceptibility levels is in constant research and development. As a result, numerous modelling techniques and approaches have been adopted by scholars, implementing these models at different scales and with different terrains, in search of the best-performing strategy. Nevertheless, a direct comparison is not possible unless the strategies are implemented under the same environmental conditions and scenarios. The aim of this work is to implement three statistical-based models (Statistical Index, Logistic Regression, and Random Forest) at the basin scale, using various scenarios for the input datasets (terrain variables), training samples and ratios, and validation metrics. A reassessment of the original input data was carried out to improve the model performance. In total, 79 maps were obtained using different combinations with some highly satisfactory outcomes and others that are barely acceptable. Random Forest achieved the highest scores in most of the cases, proving to be a reliable modelling approach. While Statistical Index passes the evaluation tests, most of the resulting maps were considered unreliable. This research highlighted the importance of a complete and up-to-date landslide inventory, the knowledge of local conditions, as well as the pre- and post-analysis evaluation of the input and output combinations. Numéro de notice : A2020-695 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE Nature : Article DOI : 10.1007/s12518-020-00344-1 date de publication en ligne : 09/11/2020 En ligne : https://doi.org/10.1007/s12518-020-00344-1 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96244 [article]

Exploratory bivariate and multivariate geovisualizations of a social vulnerability index / Georgianna Strode in Cartographic perspectives, n° 95 (July 2020)  

Public [article]  inCartographic perspectives > n° 95 (July 2020) . - 19 p. Titre : Exploratory bivariate and multivariate geovisualizations of a social vulnerability index Type de document : Article/Communication Auteurs : Georgianna Strode, Auteur ; Victor Mesev, Auteur ; Susanne Bleisch, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : 19 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes descripteurs IGN] analyse bivariée [Termes descripteurs IGN] analyse multivariée [Termes descripteurs IGN] analyse spatiale [Termes descripteurs IGN] carte thématique [Termes descripteurs IGN] données socio-économiques [Termes descripteurs IGN] ethnie [Termes descripteurs IGN] Floride (Etats-Unis) [Termes descripteurs IGN] formule d'Euler [Termes descripteurs IGN] planification stratégique [Termes descripteurs IGN] prevention [Termes descripteurs IGN] santé [Termes descripteurs IGN] signe conventionnel [Termes descripteurs IGN] sociologie [Termes descripteurs IGN] vulnérabilité [Vedettes matières IGN] Géovisualisation Résumé : (auteur) In the United States, the Centers for Disease Control and Prevention (CDC) is the national agency that conducts and supports public health research and practice. Among the CDC’s many achievements is the development of a social vulnerability index (SVI) to aid planners and emergency responders when identifying vulnerable segments of the population, especially during natural hazard events. The index includes an overall social vulnerability ranking as well as four individual themes: socioeconomic, household composition & disability, ethnicity & language, and housing & transportation. This makes the SVI dataset multivariate, but it is typically viewed via maps that show one theme at a time. This paper explores a suite of cartographic techniques that can represent the SVI beyond the univariate view. Specifically, we recommend three techniques: (1) bivariate mapping to illustrate overall vulnerability and population density, (2) multivariate mapping using cartographic glyphs to disaggregate levels of the four vulnerability themes, and (3) visual analytics using Euler diagrams to depict overlap between the vulnerability themes. The CDC’s SVI, and by extension, vulnerability indices in other countries, can be viewed in a variety of cartographic forms that illustrate the location of vulnerable groups of society. Viewing data from various perspectives can facilitate the understanding and analysis of the growing amount and complexity of data. Numéro de notice : A2020-750 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.14714/CP95.1569 date de publication en ligne : 17/03/2020 En ligne : https://doi.org/10.14714/CP95.1569 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96404 [article]

The image of subsurface geology / Ane Bang-Kittilsen in International journal of cartography, Vol 6 n° 2 (July 2020)  

Public [article]  inInternational journal of cartography > Vol 6 n° 2 (July 2020) . - pp 222 - 240 Titre : The image of subsurface geology Type de document : Article/Communication Auteurs : Ane Bang-Kittilsen, Auteur Année de publication : 2020 Article en page(s) : pp 222 - 240 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Rédaction cartographique [Termes descripteurs IGN] analyse sémantique [Termes descripteurs IGN] analyse visuelle [Termes descripteurs IGN] carte géologique [Termes descripteurs IGN] communication cartographique [Termes descripteurs IGN] croquis [Termes descripteurs IGN] enquête [Termes descripteurs IGN] géologie [Termes descripteurs IGN] langage cartographique [Termes descripteurs IGN] pictogramme [Termes descripteurs IGN] sémiologie graphique [Termes descripteurs IGN] symbole graphique [Termes descripteurs IGN] utilisateur [Termes descripteurs IGN] zone urbaine Résumé : (auteur) There is an underuse of geological knowledge in society. Therefore, an unused potential of more informed decision-making and planning as well as improved solutions on societal challenges exist. The aim of this study was to better understand the geological map user and to improve the usability of geological map products. With the aim of improving graphical communication through maps and images, visual research methods are used. The sketch map method, which has been used since the 1960s, is used here to elicit information about people and their image subsurface geology of a city. The participants include students in area planning and experts within geology. Content, semiotic and visual analyses were performed on the sketches produced by the participants. The results show limited knowledge of geology and a lack of common geological language, both graphical and linguistic. Improved ways of representing the subsurface are identified, which can be used as input to more intuitive future designs. Adapting to the user’s image of subsurface geology, usability could be increased by using plain language, adding landmarks, pictographic symbols and patterns to geological visualizations. This could potentially lower the user threshold, trigger interest and raise the awareness of local urban geology. Numéro de notice : A2020-375 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/23729333.2019.1637489 date de publication en ligne : 07/08/2019 En ligne : https://doi.org/10.1080/23729333.2019.1637489 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95309 [article]

Mountain summit detection with Deep Learning: evaluation and comparison with heuristic methods / Rocio Nahime Torres in Applied geomatics, vol 12 n° 2 (June 2020)  

Public [article]  inApplied geomatics > vol 12 n° 2 (June 2020) . - pp 225 – 246 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 descripteurs IGN] apprentissage profond [Termes descripteurs IGN] base de données altimétriques [Termes descripteurs IGN] classification floue [Termes descripteurs IGN] collecte de données [Termes descripteurs IGN] données localisées des bénévoles [Termes descripteurs IGN] figuré du terrain [Termes descripteurs IGN] méthode heuristique [Termes descripteurs IGN] modèle numérique de surface [Termes descripteurs IGN] montagne [Termes descripteurs IGN] OpenStreetMap [Termes descripteurs IGN] sommet (relief) [Termes descripteurs IGN] système d'information géographique Résumé : (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 [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)  

Permalink

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)  

Permalink

Variable DEM generalization using local entropy for terrain representation through scale / Paulo Raposo in International journal of cartography, Vol 6 n° 1 (March 2020)  

Permalink

Robust 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)  

Permalink

Visual clutter reduction in zoomable proportional point symbol maps / Tomasz Opach in Cartography and Geographic Information Science, Vol 46 n° 4 (July 2019)  

Permalink

Cartographic symbol design considerations for the space–time cube / Christopher League in Cartographic journal (the), Vol 56 n° 2 (May 2019)  

Permalink

Comparing finite and infinitesimal map distortion measures / Krisztian Kerkovits in International journal of cartography, vol 5 n° 1 (March 2019)  

Permalink

Map symbols for crisis mapping : challenges and prospects / John C. Kostelnick in Cartographic journal (the), Vol 56 n° 1 (February 2019)  

Permalink

Automated Swiss-style relief shading and rock hachuring / Roman Geisthövel in Cartographic journal (the), Vol 55 n° 4 (November 2018)  

Permalink

Star and polyline glyphs in a grid plot and on a map display: which perform better? / Tomasz Opach in Cartography and Geographic Information Science, Vol 45 n° 5 (August 2018)  

Permalink