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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] typographie [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 vulner-ability 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 indi-vidual 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 recom-mend 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 in-dices 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)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 34 n° 5 (May 2020) . - pp 1022 - 1050 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 descripteurs IGN] analyse géovisuelle [Termes descripteurs IGN] cartographie d'urgence [Termes descripteurs IGN] cartographie des risques [Termes descripteurs IGN] inondation [Termes descripteurs IGN] itinéraire [Termes descripteurs IGN] planification [Termes descripteurs IGN] représentation cartographique [Termes descripteurs IGN] sémiologie graphique [Termes descripteurs IGN] symbole graphique Résumé : (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 [article]

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