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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 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 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 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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GeoTxt: A scalable geoparsing system for unstructured text geolocation / Morteza Karimzadeh in Transactions in GIS, vol 23 n° 1 (February 2019)  

Public [article]  inTransactions in GIS > vol 23 n° 1 (February 2019) . - pp 118 - 136 Titre : GeoTxt: A scalable geoparsing system for unstructured text geolocation Type de document : Article/Communication Auteurs : Morteza Karimzadeh, Auteur ; Scott Pezanowski, Auteur ; Alan M. MacEachren, Auteur ; Jan Oliver Wallgrün, Auteur Année de publication : 2019 Article en page(s) : pp 118 - 136 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Informatique [Termes descripteurs IGN] analyse sémantique [Termes descripteurs IGN] analyse syntaxique [Termes descripteurs IGN] appariement de données localisées [Termes descripteurs IGN] corpus [Termes descripteurs IGN] données issues des réseaux sociaux [Termes descripteurs IGN] interface de programmation [Termes descripteurs IGN] méthode heuristique [Termes descripteurs IGN] reconnaissance de noms [Termes descripteurs IGN] répertoire toponymique [Termes descripteurs IGN] réseau sémantique [Termes descripteurs IGN] toponyme [Termes descripteurs IGN] traitement du langage naturel Résumé : (auteur) In this article, we present GeoTxt, a scalable geoparsing system for the recognition and geolocation of place names in unstructured text. GeoTxt offers six named entity recognition (NER) algorithms for place name recognition, and utilizes an enterprise search engine for the indexing, ranking, and retrieval of toponyms, enabling scalable geoparsing for streaming text. GeoTxt offers a flexible application programming interface (API), allowing for customized attribute and/or spatial ranking of retrieved toponyms. We evaluate the system on a corpus of manually geo‐annotated tweets. First, we benchmark the performance of the six NERs that GeoTxt provides access to. Second, we assess GeoTxt toponym resolution accuracy incrementally, demonstrating improvements in toponym resolution achieved (or not achieved) by adding specific heuristics and disambiguation methods. Compared to using the GeoNames web service, GeoTxt's toponym resolution demonstrates a 20% accuracy gain. Our results show that places mentioned in the same tweet do not tend to be geographically proximate. Numéro de notice : A2019-091 Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12510 date de publication en ligne : 16/01/2019 En ligne : https://doi.org/10.1111/tgis.12510 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92238 [article]

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

Public Titre : Data linking by indirect spatial referencing systems, EuroSDR - EuroGeographics seminar, September 5th - 6th, 2018 - Paris, France Type de document : Actes de congrès Auteurs : Bénédicte Bucher , Auteur ; Esa Tiainen, Auteur ; Thomas Ellett von Brasch, Auteur ; Elise Acheson, Auteur ; Dominique Laurent, Auteur ; Sylvain Boissel, Auteur Congrès : EuroSDR & EuroGeographics 2018, Seminar Data Linking by Indirect Spatial Referencing Systems (5 - 6 septembre 2018; Paris, France), Commanditaire Editeur : Dublin : European Spatial Data Research EuroSDR Année de publication : 2019 Collection : EuroSDR Workshop report Projets : 1-Pas de projet / Importance : 26 p. Format : 21 x 30 cm Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web [Termes descripteurs IGN] cohérence des données [Termes descripteurs IGN] ontologie [Termes descripteurs IGN] répertoire toponymique [Termes descripteurs IGN] toponyme [Termes descripteurs IGN] Web des données Note de contenu : 1 INTRODUCTION 2 PRESENTATIONS SUMMARY 2.1 Gazetteers for linking text tospace: experiences with contrasting corpora, Elise Acheson, University of Zurich 2.2 Georef - Service and Development platform:Research data pilot overview, Esa Tiainen, National Land Survey of Finland 2.3 Assessing the importance of named places: benefits and difficulties, Dominique Laurent, IGN France 2.4 Designing Data projects, how to value geographical heritage data with state of the art solutions?, Julien Homo, Kévin Darty, Foxcub 2.5 Finnish Linked Data pilots, Kai Koistinen, National Land Survey of Finland 2.6 The challenge of linking or integrating data on Buildings, Dominique Laurent, IGN France 2.7 Administrative Units as Linked Open Data – A case study from the Norwegian Mapping Authority, Thomas Ellett, Kartverket 2.8 Wikidata, a short introduction, Julien Boissel, Wikimedia foundation 2.9 Linear indirect reference systems to interconnect data in transportation applications, Alain Chaumet, ENSG-Valilab 3 COMMON FINDINGS ISSUED FROM WRAP UP SESSIONS 3.1 Need for ontologies of places and of digital assets 3.2 Consistency in an open world 3.3 Computing, maintaining and sharing links 3.4 Communities, commitments, authorities 4 CONCLUSION AND FURTHER AREAS OF RESEARCHAND DEVELOPMENTS 5 ACKNOWLEDGEMENTS Numéro de notice : 15197 Thématique : GEOMATIQUE Nature : Rapport sur congrès nature-HAL : DirectOuvrColl/Actes DOI : sans En ligne : http://www.eurosdr.net/sites/default/files/uploaded_files/eurosdr_data_linking_b [...] Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93762

Diversity and transparency in (volunteered) geoinformation practices / Tilo Felgenhauer in GI Forum, vol 2018 n° 2 ([01/09/2018])  

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Are prominent mountains frequently mentioned in text? Exploring the spatial expressiveness of text frequency / Curdin Derungs in International journal of geographical information science IJGIS, vol 32 n° 5-6 (May - June 2018)  

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Context-aware automated interpretation of elaborate natural language descriptions of location through learning from empirical data / Kristin Stock in International journal of geographical information science IJGIS, vol 32 n° 5-6 (May - June 2018)  

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Crowdsourcing the character of a place : Character‐level convolutional networks for multilingual geographic text classification / Benjamin Adams in Transactions in GIS, vol 22 n° 2 (April 2018)  

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Désambiguïsation des entités spatiales par apprentissage actif / Amal Chihaoui in Revue internationale de géomatique, vol 28 n° 2 (avril - juin 2018)  

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

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Toponym matching through deep neural networks / Rui Santos in International journal of geographical information science IJGIS, vol 32 n° 1-2 (January - February 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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Analyse et cartographie des sentiments dans des récits de vie de migrants / Catherine Dominguès (2017)  

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European handbook of crowdsourced geographic information, ch. 8. Quality analysis of the Parisian OSM toponyms evolution / Vyron Antoniou (2016)    

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