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Can we characterize river corridor evolution at a continental scale from historical topographic maps? A first assessment from the comparison of four countries / J. Horacio Garcia in River Research and Applications, vol 36 n° 6 (July 2020)
[article]
Titre : Can we characterize river corridor evolution at a continental scale from historical topographic maps? A first assessment from the comparison of four countries Type de document : Article/Communication Auteurs : J. Horacio Garcia, Auteur ; Samuel Dunesme , Auteur ; Hervé Piegay, Auteur Année de publication : 2020 Projets : EUR H20'Lyon / Article en page(s) : pp 934 - 946 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] Belgique
[Termes IGN] carte ancienne
[Termes IGN] corridor biologique
[Termes IGN] dix-neuvième siècle
[Termes IGN] forêt ripicole
[Termes IGN] géomorphologie locale
[Termes IGN] rivière
[Termes IGN] Suisse
[Termes IGN] vectorisationRésumé : (auteur) National historical map resources are assessed in four European countries to characterize river corridor features and associated channel changes, as well as identify issues limiting or promoting geomorphic assessment procedures at a continental scale. A geomorphic audit that launches potential data for diagnosis from reach to continental scales could offer a good resource for biology and ecology managers of river authorities or government agencies and engineers. The assessment compares the resources available by country in terms of period covered, spatial scale, history and chronology, and representation of the fluvial corridor features. We then applied the Historical Maps Vectorization Toolbox, initially developed for vectorizing river corridors from French maps, to detect and extract flow channels, unvegetated bars and riparian vegetation patches from historical topographical maps. We found that (a) it is difficult to apply an audit of channel changes to the whole continental scale because map legends differ between countries due to geographic and political specificity; (b) there exists an opportunity to get assessment information in all countries at reach or national scale where map resources are available; (c) the highest potential is observed in Switzerland and Belgium where there is high quality national map coverage from the 19th century; and (d) the algorithm Historical Maps Vectorization Toolbox applied to map resources works well with any of the countries, and its widespread application is encouraging. Numéro de notice : A2020-362 Affiliation des auteurs : ENSG+Ext (2012-2019) Thématique : FORET/GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1002/rra.3582 Date de publication en ligne : 30/12/2019 En ligne : https://doi.org/10.1002/rra.3582 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95333
in River Research and Applications > vol 36 n° 6 (July 2020) . - pp 934 - 946[article]Regionalization of flood magnitudes using the ecological attributes of watersheds / Bahman Jabbarian Amiri in Geocarto international, vol 35 n° 9 ([01/07/2020])
[article]
Titre : Regionalization of flood magnitudes using the ecological attributes of watersheds Type de document : Article/Communication Auteurs : Bahman Jabbarian Amiri, Auteur ; Bahareh Baheri, Auteur ; Nicola Fohrer, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 917 - 933 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] bassin hydrographique
[Termes IGN] Caspienne, mer
[Termes IGN] crue
[Termes IGN] débit
[Termes IGN] estimation quantitative
[Termes IGN] humidité du sol
[Termes IGN] inondation
[Termes IGN] modèle de simulation
[Termes IGN] occupation du sol
[Termes IGN] prévention des risques
[Termes IGN] régionalisation (segmentation)
[Termes IGN] ressources en eau
[Termes IGN] utilisation du sol
[Termes IGN] zone inondableRésumé : (auteur) Estimating flood discharge at ungauged sites is a significant challenge facing water resources planners and engineers during the planning and design of hydraulic structures, managing flood prone zones, and operating artificial waterbodies. Developing more robust models to improve the reliability of flood discharge estimations is thus very useful. The role of ecological attributes including land use/land cover (LULC), hydrologic soil groups (HSG), and watershed physical characteristics (area, main stream length, average slope), and watershed shape coefficients (form, compactness, circularity, and elongation) in explaining the overall variation in flood magnitude in 39 watersheds, located in the southern basin of the Caspian Sea, was investigated. As the LULC and HSG were found to play a significant role in explaining total variation (40–89%) in flood magnitudes, their inclusion in the estimation of flood magnitudes can provide more reliable estimates of flood risk and magnitude. Numéro de notice : A2020-428 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2018.1552321 Date de publication en ligne : 07/02/2019 En ligne : https://doi.org/10.1080/10106049.2018.1552321 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95494
in Geocarto international > vol 35 n° 9 [01/07/2020] . - pp 917 - 933[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 059-2020091 RAB Revue Centre de documentation En réserve L003 Disponible 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]Traffic signal detection from in-vehicle GPS speed profiles using functional data analysis and machine learning / Yann Méneroux in International Journal of Data Science and Analytics JDSA, vol 10 n° 1 (June 2020)
[article]
Titre : Traffic signal detection from in-vehicle GPS speed profiles using functional data analysis and machine learning Type de document : Article/Communication Auteurs : Yann Méneroux , Auteur ; Arnaud Le Guilcher , Auteur ; Guillaume Saint Pierre, Auteur ; Mohammad Ghasemi Hamed, Auteur ; Sébastien Mustière , Auteur ; Olivier Orfila, Auteur Année de publication : 2020 Projets : 1-Pas de projet / Article en page(s) : pp 101 - 119 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] analyse fonctionnelle (mathématiques)
[Termes IGN] apprentissage profond
[Termes IGN] carte routière
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] contenu généré par les utilisateurs
[Termes IGN] détection d'objet
[Termes IGN] données routières
[Termes IGN] feu de circulation
[Termes IGN] inférence
[Termes IGN] reconnaissance de formes
[Termes IGN] signalisation routière
[Termes IGN] trace GPS
[Termes IGN] trafic routier
[Termes IGN] transformation en ondelettes
[Termes IGN] vitesseRésumé : (auteur) The increasing availability of large-scale global positioning system data stemming from in-vehicle-embedded terminal devices enables the design of methods deriving road network cartographic information from drivers’ recorded traces. Some machine learning approaches have been proposed in the past to train automatic road network map inference, and recently this approach has been successfully extended to infer road attributes as well, such as speed limitation or number of lanes. In this paper, we address the problem of detecting traffic signals from a set of vehicle speed profiles, under a classification perspective. Each data instance is a speed versus distance plot depicting over a hundred profiles on a 100-m-long road span. We proposed three different ways of deriving features: The first one relies on the raw speed measurements; the second one uses image recognition techniques; and the third one is based on functional data analysis. We input them into most commonly used classification algorithms, and a comparative analysis demonstrated that a functional description of speed profiles with wavelet transforms seems to outperform the other approaches with most of the tested classifiers. It also highlighted that random forests yield an accurate detection of traffic signals, regardless of the chosen feature extraction method, while keeping a remarkably low confusion rate with stop signs. Numéro de notice : A2020-336 Affiliation des auteurs : LASTIG COGIT+Ext (2012-2019) Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s41060-019-00197-x Date de publication en ligne : 04/10/2019 En ligne : https://doi.org/10.1007/s41060-019-00197-x Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93755
in International Journal of Data Science and Analytics JDSA > vol 10 n° 1 (June 2020) . - pp 101 - 119[article]Documents numériques
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Traffic signal detection ... - preprintAdobe Acrobat PDF How much do we learn from addresses? On the syntax, semantics and pragmatics of addressing systems / Ali Javidaneh in ISPRS International journal of geo-information, vol 9 n° 5 (May 2020)
[article]
Titre : How much do we learn from addresses? On the syntax, semantics and pragmatics of addressing systems Type de document : Article/Communication Auteurs : Ali Javidaneh, Auteur ; Farid Karimipour, Auteur ; Negar Alinaghi, Auteur Année de publication : 2020 Article en page(s) : 27 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] adresse postale
[Termes IGN] appariement d'adresses
[Termes IGN] cognition
[Termes IGN] géocodage par adresse postale
[Termes IGN] modèle orienté agent
[Termes IGN] représentation mentale spatiale
[Termes IGN] segmentation sémantique
[Termes IGN] structure syntaxiqueRésumé : (auteur) An address is a specification that refers to a unique location on Earth. While there has been a considerable amount of research on the syntactic structure of addressing systems in order to evaluate and improve their quality, aspects of semantics and pragmatics have been less explored. An address is primarily associated by humans to the elements of their spatial mental representations, but may also influence their spatial knowledge and activities through the level of detail it provides. Therefore, it is not only important how addressing components are structured, but it is also of interest to study their meaning as well as the pragmatics in relation to an interpreting agent. This article studies three forms of addresses (i.e., structured as in Austria, semi-formal as in Japan, and descriptive as in Iran) under the principles of semiotics (i.e., through levels of syntax, semantics, and pragmatics). Syntax is discussed through formal definitions of the addressing systems, while semantics and pragmatics are assessed through an agent-based model to explore how they influence spatial knowledge acquisition and growth. Numéro de notice : A2020-302 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi9050317 Date de publication en ligne : 11/05/2020 En ligne : https://doi.org/10.3390/ijgi9050317 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95142
in ISPRS International journal of geo-information > vol 9 n° 5 (May 2020) . - 27 p.[article]Urban climate services: climate impact projections and their uncertainties at city scale / Bert Van Schaeybroeck in FMI's climate bulletin research letters, vol 2020 n° 1 (Spring 2020)PermalinkIFC schemas in ISO/TC 211 compliant UML for improved interoperability between BIM and GIS / Knut Jetlund in ISPRS International journal of geo-information, vol 9 n° 4 (April 2020)PermalinkRecognizing linear building patterns in topographic data by using two new indices based on Delaunay triangulation / Xianjin He in ISPRS International journal of geo-information, vol 9 n° 4 (April 2020)PermalinkCity-descriptive input data for urban climate models: Model requirements, data sources and challenges / Valéry Masson in Urban climate, vol 31 (March 2020)PermalinkA deep learning architecture for semantic address matching / Yue Lin in International journal of geographical information science IJGIS, vol 34 n° 3 (March 2020)PermalinkA proposal for modeling indoor–outdoor spaces through indoorGML, open location code and OpenStreetMap / Ruben Cantarero Navarro in ISPRS International journal of geo-information, vol 9 n° 3 (March 2020)PermalinkPermalinkAnalyse hydrologique du réseau de drainage de la zone sud de la métropole nantaise pour une meilleure gestion des eaux pluviales / Anna Guézénoc (2020)PermalinkPermalinkCalcul d’une emprise de carte à partir du texte d’un article de presse / Clément Beauvallet (2020)Permalink