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Decolonizing world heritage maps using indigenous toponyms, stories, and interpretive attributes / Mark Palmer in Cartographica, vol 55 n° 3 (Fall 2020)
[article]
Titre : Decolonizing world heritage maps using indigenous toponyms, stories, and interpretive attributes Type de document : Article/Communication Auteurs : Mark Palmer, Auteur ; Cadey Korson, Auteur Année de publication : 2020 Article en page(s) : pp 183 - 192 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Toponymie
[Termes IGN] Australie
[Termes IGN] Canada
[Termes IGN] carte administrative
[Termes IGN] Etats-Unis
[Termes IGN] ethnologie
[Termes IGN] histoire
[Termes IGN] Nouvelle-Zélande
[Termes IGN] patrimoine culturel
[Termes IGN] représentation géographique
[Termes IGN] système d'information géographique
[Termes IGN] toponymie localeRésumé : (auteur) Maps and GIS used for the nomination and subsequent management of UNESCO World Heritage sites have primarily served bureaucratic resource management purposes. However, bureaucratic maps offer an opportunity to represent associative cultural landscapes, intangible cultural elements, and the geographies of Indigenous peoples. Indigenous toponyms can be found on many World Heritage maps for sites located within settler societies such as New Zealand, Australia, the United States, and Canada. Currently, bureaucratic heritage maps do not emphasize or even have a method for presenting the meaning and significance of Indigenous toponyms. Instead, the names are represented as static, inanimate objects void of meaning. This article presents archival evidence that bureaucratic state maps found within some UNESCO World Heritage nomination dossiers and resource management plans contain Indigenous cartographic elements that Indigenous communities could use as the basis for creating Indigital story maps. Numéro de notice : A2020-604 Affiliation des auteurs : non IGN Thématique : TOPONYMIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3138/cart-2019-0014 Date de publication en ligne : 30/09/2020 En ligne : https://doi.org/10.3138/cart-2019-0014 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95961
in Cartographica > vol 55 n° 3 (Fall 2020) . - pp 183 - 192[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 031-2020031 SL Revue Centre de documentation Revues en salle Disponible
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Titre : Making the Coming Home Map Type de document : Article/Communication Auteurs : Margaret W. Pearce, Auteur ; Stephen J. Hornsby, Auteur Année de publication : 2020 Article en page(s) : pp 170 - 176 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Cartographie
[Termes IGN] Canada
[Termes IGN] cartographie historique
[Termes IGN] colonisation
[Termes IGN] conception cartographique
[Termes IGN] histoire
[Termes IGN] représentation cartographique
[Termes IGN] toponymie localeRésumé : (auteur) To mark Canada 150, the Canadian-American Center at the University of Maine released a new map, Coming Home to Indigenous Place Names in Canada. The map is intended to honour Indigenous place names in Canada and the assertion of Indigenous authority through place naming. It was made possible by permissions and contributions from First Nation, Métis, and Inuit communities, organizations, and language speakers. In this article, we explain how and why this map came to be made at the Canadian-American Center. We examine the methods we used to request and include place names and how this process in turn informed the design of the visual language of the map. Numéro de notice : A2020-603 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3138/cart-2019-0012 Date de publication en ligne : 30/09/2020 En ligne : https://doi.org/10.3138/cart-2019-0012 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95958
in Cartographica > vol 55 n° 3 (Fall 2020) . - pp 170 - 176[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 031-2020031 SL Revue Centre de documentation Revues en salle Disponible A name‐led approach to profile urban places based on geotagged Twitter data / Juntao Lai in Transactions in GIS, Vol 24 n° 4 (August 2020)
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Titre : A name‐led approach to profile urban places based on geotagged Twitter data Type de document : Article/Communication Auteurs : Juntao Lai, Auteur ; Guy Lansley, Auteur ; James Haworth, Auteur ; Tao Cheng, Auteur Année de publication : 2020 Article en page(s) : 22 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web
[Termes IGN] analyse spatiale
[Termes IGN] approche participative
[Termes IGN] données localisées
[Termes IGN] données localisées des bénévoles
[Termes IGN] espace urbain
[Termes IGN] Foursquare
[Termes IGN] Londres
[Termes IGN] point d'intérêt
[Termes IGN] réseau social
[Termes IGN] réseau social géodépendant
[Termes IGN] site urbain
[Termes IGN] toponyme
[Termes IGN] TwitterRésumé : (auteur) Place is a concept that is fundamental to how we orientate and communicate space in our everyday lives. Crowdsourced social media data present a valuable opportunity to develop bottom‐up inferences of places that are integral to social activities and settings. Conventional location‐led approaches use a predefined spatial unit to associate data and space with places, which cannot capture the richness of urban places (i.e., spatial extents and their dynamic functions). This article develops a name‐led framework to overcome these limitations in using social media data to study urban places. The framework first derives place names from georeferenced Twitter data combining text mining and spatial point pattern analysis, then estimates the spatial extents by spatial clustering, and further extracts their dynamic functions with time, which makes up a complete place profile. The framework is tested on a case study in Camden Borough, London and the results are evaluated through comparisons to the Foursquare point of interest data. This name‐led approach enables the shift from space‐based analysis to place‐based analysis of urban space. Numéro de notice : A2020-670 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/SOCIETE NUMERIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12599 Date de publication en ligne : 05/12/2019 En ligne : https://doi.org/10.1111/tgis.12599 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96155
in Transactions in GIS > Vol 24 n° 4 (August 2020) . - 22 p.[article]GeoNat v1.0: A dataset for natural feature mapping with artificial intelligence and supervised learning / Samantha T. Arundel in Transactions in GIS, Vol 24 n° 3 (June 2020)
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Titre : GeoNat v1.0: A dataset for natural feature mapping with artificial intelligence and supervised learning Type de document : Article/Communication Auteurs : Samantha T. Arundel, Auteur ; Wenwen Li, Auteur ; Sizhe Wang, Auteur Année de publication : 2020 Article en page(s) : pp 556 - 572 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle
[Termes IGN] apprentissage automatique
[Termes IGN] apprentissage dirigé
[Termes IGN] cartographie topographique
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] collecte de données
[Termes IGN] détection automatique
[Termes IGN] détection d'objet
[Termes IGN] géobalise
[Termes IGN] toponyme
[Termes IGN] United States Geological SurveyRésumé : (Auteur) Machine learning allows “the machine” to deduce the complex and sometimes unrecognized rules governing spatial systems, particularly topographic mapping, by exposing it to the end product. Often, the obstacle to this approach is the acquisition of many good and labeled training examples of the desired result. Such is the case with most types of natural features. To address such limitations, this research introduces GeoNat v1.0, a natural feature dataset, used to support artificial intelligence‐based mapping and automated detection of natural features under a supervised learning paradigm. The dataset was created by randomly selecting points from the U.S. Geological Survey’s Geographic Names Information System and includes approximately 200 examples each of 10 classes of natural features. Resulting data were tested in an object‐detection problem using a region‐based convolutional neural network. The object‐detection tests resulted in a 62% mean average precision as baseline results. Major challenges in developing training data in the geospatial domain, such as scale and geographical representativeness, are addressed in this article. We hope that the resulting dataset will be useful for a variety of applications and shed light on training data collection and labeling in the geospatial artificial intelligence domain. Numéro de notice : A2020-245 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12633 Date de publication en ligne : 08/05/2020 En ligne : https://doi.org/10.1111/tgis.12633 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95307
in Transactions in GIS > Vol 24 n° 3 (June 2020) . - pp 556 - 572[article]NeuroTPR: A neuro‐net toponym recognition model for extracting locations from social media messages / Jimin Wang in Transactions in GIS, Vol 24 n° 3 (June 2020)
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Titre : NeuroTPR: A neuro‐net toponym recognition model for extracting locations from social media messages Type de document : Article/Communication Auteurs : Jimin Wang, Auteur ; Yingjie Hu, Auteur ; Kenneth Joseph, Auteur Année de publication : 2020 Article en page(s) : pp 719 - 735 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web
[Termes IGN] catastrophe naturelle
[Termes IGN] données issues des réseaux sociaux
[Termes IGN] données localisées des bénévoles
[Termes IGN] flux de travaux
[Termes IGN] géolocalisation
[Termes IGN] précision sémantique
[Termes IGN] reconnaissance de noms
[Termes IGN] réseau neuronal récurrent
[Termes IGN] réseau social
[Termes IGN] toponymeRésumé : (auteur) Social media messages, such as tweets, are frequently used by people during natural disasters to share real‐time information and to report incidents. Within these messages, geographic locations are often described. Accurate recognition and geolocation of these locations are critical for reaching those in need. This article focuses on the first part of this process, namely recognizing locations from social media messages. While general named entity recognition tools are often used to recognize locations, their performance is limited due to the various language irregularities associated with social media text, such as informal sentence structures, inconsistent letter cases, name abbreviations, and misspellings. We present NeuroTPR, which is a Neuro‐net ToPonym Recognition model designed specifically with these linguistic irregularities in mind. Our approach extends a general bidirectional recurrent neural network model with a number of features designed to address the task of location recognition in social media messages. We also propose an automatic workflow for generating annotated data sets from Wikipedia articles for training toponym recognition models. We demonstrate NeuroTPR by applying it to three test data sets, including a Twitter data set from Hurricane Harvey, and comparing its performance with those of six baseline models. Numéro de notice : A2020-445 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12627 Date de publication en ligne : 14/05/2020 En ligne : https://doi.org/10.1111/tgis.12627 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95508
in Transactions in GIS > Vol 24 n° 3 (June 2020) . - pp 719 - 735[article]The place names of French Guiana in the face of the geoweb: Between data sovereignty, indigenous knowledge, and cartographic deregulation / Matthieu Noucher in Cartographica, vol 55 n° 1 (Spring 2020)PermalinkCalcul d’une emprise de carte à partir du texte d’un article de presse / Clément Beauvallet (2020)PermalinkDes empreintes cartographiques : restitution de données géohistoriques à partir de la Carte de France de Cassini, 1750-1789 / Bertrand Duménieu in Cartes & Géomatique, n° 241-242 (décembre 2019)PermalinkMapping urban fingerprints of odonyms automatically extracted from French novels / Ludovic Moncla in International journal of geographical information science IJGIS, vol 33 n° 12 (December 2019)PermalinkChinese and Russian Language Equivalents of the IAU Gazetteer of Planetary Nomenclature: an overview of planetary toponym localization methods / Henrik Hargitai in Cartographic journal (the), Vol 56 n° 4 (November 2019)PermalinkValidating the use of object-based image analysis to map commonly recognized landform features in the United States / Samantha T. Arundel in Cartography and Geographic Information Science, Vol 46 n° 5 (September 2019)PermalinkA natural language processing and geospatial clustering framework for harvesting local place names from geotagged housing advertisements / Yingjie Hu in International journal of geographical information science IJGIS, Vol 33 n° 3-4 (March - April 2019)PermalinkGeoTxt: A scalable geoparsing system for unstructured text geolocation / Morteza Karimzadeh in Transactions in GIS, vol 23 n° 1 (February 2019)PermalinkData linking by indirect spatial referencing systems, [report of] EuroSDR - EuroGeographics seminar, September 5th - 6th, 2018 - Paris, France / Bénédicte Bucher (2019)PermalinkRepérage et identification automatiques de noms de lieux avec variations d'écriture dans des corpus / Mathilde Jouvel-Triollet (2019)Permalink