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Sherloc: a knowledge-driven algorithm for geolocating microblog messages at sub-city level / Laura Di Rocco in International journal of geographical information science IJGIS, vol 35 n° 1 (January 2021)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 35 n° 1 (January 2021) . - pp 84 - 115 Titre : Sherloc: a knowledge-driven algorithm for geolocating microblog messages at sub-city level Type de document : Article/Communication Auteurs : Laura Di Rocco, Auteur ; Michela Bertolotto, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 84 - 115 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes descripteurs IGN] analyse sémantique [Termes descripteurs IGN] données localisées [Termes descripteurs IGN] géolocalisation [Termes descripteurs IGN] inférence [Termes descripteurs IGN] microblogue [Termes descripteurs IGN] répertoire toponymique [Termes descripteurs IGN] système à base de connaissances [Termes descripteurs IGN] toponyme [Termes descripteurs IGN] zone urbaine Résumé : (auteur) Many solutions for coarse geolocating of users at the time they post a message exist. However, for many important applications, like traffic monitoring and event detection, finer geolocation at the level of city neighborhoods, i.e., at a sub-city level, is needed. Data-driven approaches often do not guarantee good accuracy and efficiency due to the higher number of sub-city level positions to be estimated and the low availability of balanced and large training sets. We claim that external information sources overcome limitations of data-driven approaches in achieving good accuracy for sub-city level geolocation and we present a knowledge-driven approach achieving good results once the reference area of a message is known. Our algorithm, called Sherloc, exploits toponyms in the message, extracts their semantic from a geographic gazetteer, and embeds them into a metric space that captures the semantic distance among them. We identify the semantically closest toponyms to a message and then cluster them with respect to their spatial locations. Sherloc requires no prior training, it can infer the location at sub-city level with high accuracy, and it is not limited to geolocating on a fixed spatial grid. Numéro de notice : A2021-021 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2020.1764003 date de publication en ligne : 16/06/2020 En ligne : https://doi.org/10.1080/13658816.2020.1764003 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96521 [article]

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)  

Public [article]  inTransactions in GIS > Vol 24 n° 4 (August 2020) . - 22 p. 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 descripteurs IGN] analyse spatiale [Termes descripteurs IGN] approche participative [Termes descripteurs IGN] données localisées [Termes descripteurs IGN] données localisées des bénévoles [Termes descripteurs IGN] espace urbain [Termes descripteurs IGN] Foursquare [Termes descripteurs IGN] Londres [Termes descripteurs IGN] point d'intérêt [Termes descripteurs IGN] réseau social [Termes descripteurs IGN] réseau social géodépendant [Termes descripteurs IGN] site urbain [Termes descripteurs IGN] toponyme [Termes descripteurs IGN] Twitter Résumé : (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 [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)  

Public [article]  inTransactions in GIS > Vol 24 n° 3 (June 2020) . - pp 556 - 572 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 descripteurs IGN] apprentissage automatique [Termes descripteurs IGN] apprentissage dirigé [Termes descripteurs IGN] cartographie topographique [Termes descripteurs IGN] classification par réseau neuronal convolutif [Termes descripteurs IGN] collecte de données [Termes descripteurs IGN] détection automatique [Termes descripteurs IGN] détection d'objet [Termes descripteurs IGN] géoétiquetage [Termes descripteurs IGN] toponyme [Termes descripteurs IGN] United States Geological Survey Résumé : (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 [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)  

Public [article]  inTransactions in GIS > Vol 24 n° 3 (June 2020) . - pp 719 - 735 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 descripteurs IGN] catastrophe naturelle [Termes descripteurs IGN] données issues des réseaux sociaux [Termes descripteurs IGN] données localisées des bénévoles [Termes descripteurs IGN] flux de travaux [Termes descripteurs IGN] géolocalisation [Termes descripteurs IGN] précision sémantique [Termes descripteurs IGN] reconnaissance de noms [Termes descripteurs IGN] réseau neuronal récurrent [Termes descripteurs IGN] réseau social [Termes descripteurs IGN] toponyme Résumé : (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 [article]

Chinese 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)  

Public [article]  inCartographic journal (the) > Vol 56 n° 4 (November 2019) . - pp 335 - 354 Titre : Chinese and Russian Language Equivalents of the IAU Gazetteer of Planetary Nomenclature: an overview of planetary toponym localization methods Type de document : Article/Communication Auteurs : Henrik Hargitai, Auteur ; Chunlai Li, Auteur ; Zhoubin Zhang, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 335 - 354 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Toponymie [Termes descripteurs IGN] caractère chinois [Termes descripteurs IGN] caractère cyrillique [Termes descripteurs IGN] interopérabilité sémantique [Termes descripteurs IGN] langue locale [Termes descripteurs IGN] langue romane [Termes descripteurs IGN] lecture de carte [Termes descripteurs IGN] nomenclature [Termes descripteurs IGN] toponyme Résumé : (Auteur) The Gazetteer of Planetary Nomenclature (GPN) is maintained by the International Astronomical Union Working Group for Planetary System Nomenclature. It contains the internationally approved forms of place names of planetary and lunar surface features. In the last decades, spacefaring and other nations have started to develop local standardized equivalents of the GPN. This initiated the development of transformation methods and created a need for auxiliary information on the names in the GPN that is not available from the database of the GPN. The creation of ‘localized’ (local language) variants of the GPN in non-Roman scripts is an unavoidable necessity, but is also a cultural need. This paper investigates the localization methods into Chinese and Russian; two nations with different scripts, and two that are spacefaring ones. The need for the creation of a localized GPN is related to the local importance of scientific papers published in the local language and the existence of locally developed and operated scientific planetary spacecraft, but exceptions exist. Numéro de notice : A2019-621 Affiliation des auteurs : non IGN Thématique : TOPONYMIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1179/1743277413Y.0000000051 date de publication en ligne : 30/06/2016 En ligne : https://doi.org/10.1179/1743277413Y.0000000051 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94497 [article]

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Data linking by indirect spatial referencing systems, [report of] EuroSDR - EuroGeographics seminar, September 5th - 6th, 2018 - Paris, France / Bénédicte Bucher (2019)  

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Lieu et nom de lieu, du texte vers sa représentation cartographique / Catherine Dominguès (2018) 

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Écriture des toponymes en français : variations entre normes et usages / Catherine Dominguès in Cahiers de lexicologie, n° 110 (2017-1)

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