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An approach for multi-scale urban building data integration and enrichment through geometric matching and semantic web / Abdulkadir Memduhoglu in Cartography and Geographic Information Science, vol 49 n° 1 (January 2022)
[article]
Titre : An approach for multi-scale urban building data integration and enrichment through geometric matching and semantic web Type de document : Article/Communication Auteurs : Abdulkadir Memduhoglu, Auteur ; Melih Basaraner, Auteur Année de publication : 2022 Article en page(s) : pp 1 - 17 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web
[Termes IGN] appariement géométrique
[Termes IGN] approche participative
[Termes IGN] bâtiment
[Termes IGN] données localisées des bénévoles
[Termes IGN] données multiéchelles
[Termes IGN] Istanbul (Turquie)
[Termes IGN] ontologie
[Termes IGN] OpenStreetMap
[Termes IGN] SWRL
[Termes IGN] web des données
[Termes IGN] web sémantique
[Termes IGN] zone urbaineRésumé : (auteur) The advent of Web 2.0 has emerged abundant but often unstructured user-generated georeferenced data, such as those from Volunteered Geographic Information (VGI) initiatives. In many cases, these data can be considered as complementary to the authoritative geospatial data. With the increasing availability of multi-source geospatial data, the efforts for geospatial data integration have gained momentum, aiming at gathering maximum information to answer sophisticated questions that cannot be answered using a single data source. Although there are various approaches employed for this purpose with different degrees of success, semantic web methods and tools have not been tested sufficiently in this scope, particularly for multi-scale urban building data integration and enrichment. Attempting to fill this gap, in this study, multi-source and multi-scale urban building data were integrated with a geometric matching method based on the overlapping area, then a geospatial ontology was developed to define multi-scale representations and detailed cardinality relations of the building features. Finally, some features from the geospatial ontology were then linked to popular knowledge bases such as DBpedia and YAGO. For the exploitation on the web, query and visualization processes were demonstrated using sample questions. The semantic web enabled to model complex cardinality of relations between the features from three different building data sets using inferencing and Semantic Web Rule Language (SWRL). The study showed that integrating different geospatial data sets as a knowledge base can facilitate answering sophisticated questions from different users. Numéro de notice : A2022-016 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/15230406.2021.1952108 Date de publication en ligne : 24/08/2021 En ligne : https://doi.org/10.1080/15230406.2021.1952108 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99147
in Cartography and Geographic Information Science > vol 49 n° 1 (January 2022) . - pp 1 - 17[article]Réservation
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Titre : Attention-based vandalism detection in OpenStreetMap Type de document : Article/Communication Auteurs : Nicolas Tempelmeier, Auteur ; Elena Demidova, Auteur Editeur : New York [Etats-Unis] : Association for computing machinery ACM Année de publication : 2022 Conférence : WWW 2022, ACM Web Conference 2022 25/04/2022 29/04/2022 Lyon online France Proceedings ACM Importance : pp 643 - 651 Note générale : bibliographie Langues : Français (fre) Descripteur : [Vedettes matières IGN] Bases de données localisées
[Termes IGN] attention (apprentissage automatique)
[Termes IGN] détection d'anomalie
[Termes IGN] fiabilité des données
[Termes IGN] historique des données
[Termes IGN] OpenStreetMapMots-clés libres : vandalisme Résumé : (auteur) OpenStreetMap (OSM), a collaborative, crowdsourced Web map, is a unique source of openly available worldwide map data, increasingly adopted in Web applications. Vandalism detection is a critical task to support trust and maintain OSM transparency. This task is remarkably challenging due to the large scale of the dataset, the sheer number of contributors, various vandalism forms, and the lack of annotated data. This paper presents Ovid - a novel attention-based method for vandalism detection in OSM. Ovid relies on a novel neural architecture that adopts a multi-head attention mechanism to summarize information indicating vandalism from OSM changesets effectively. To facilitate automated vandalism detection, we introduce a set of original features that capture changeset, user, and edit information. Furthermore, we extract a dataset of real-world vandalism incidents from the OSM edit history for the first time and provide this dataset as open data. Our evaluation conducted on real-world vandalism data demonstrates the effectiveness of Ovid. Numéro de notice : C2022-008 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Communication DOI : 10.1145/3485447.3512224 Date de publication en ligne : 25/04/2022 En ligne : https://doi.org/10.1145/3485447.3512224 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100493 Automatic identification of addresses: A systematic literature review / Paula Cruz in ISPRS International journal of geo-information, vol 11 n° 1 (January 2022)
[article]
Titre : Automatic identification of addresses: A systematic literature review Type de document : Article/Communication Auteurs : Paula Cruz, Auteur ; Leonardo Vanneschi, Auteur ; Marco Painho, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 11 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] appariement d'adresses
[Termes IGN] apprentissage automatique
[Termes IGN] apprentissage profond
[Termes IGN] base de données d'adresses
[Termes IGN] géocodage par adresse postale
[Termes IGN] Geoparsing
[Termes IGN] service fondé sur la positionRésumé : (auteur) Address matching continues to play a central role at various levels, through geocoding and data integration from different sources, with a view to promote activities such as urban planning, location-based services, and the construction of databases like those used in census operations. However, the task of address matching continues to face several challenges, such as non-standard or incomplete address records or addresses written in more complex languages. In order to better understand how current limitations can be overcome, this paper conducted a systematic literature review focused on automated approaches to address matching and their evolution across time. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed, resulting in a final set of 41 papers published between 2002 and 2021, the great majority of which are after 2017, with Chinese authors leading the way. The main findings revealed a consistent move from more traditional approaches to deep learning methods based on semantics, encoder-decoder architectures, and attention mechanisms, as well as the very recent adoption of hybrid approaches making an increased use of spatial constraints and entities. The adoption of evolutionary-based approaches and privacy preserving methods stand as some of the research gaps to address in future studies. Numéro de notice : A2022-088 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.3390/ijgi11010011 Date de publication en ligne : 29/12/2021 En ligne : https://doi.org/10.3390/ijgi11010011 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99497
in ISPRS International journal of geo-information > vol 11 n° 1 (January 2022) . - n° 11[article]
Titre : BasqueRoads: a benchmark for road network selection Type de document : Article/Communication Auteurs : Guillaume Touya , Auteur ; Azelle Courtial , Auteur Editeur : International Cartographic Association ICA - Association cartographique internationale ACI Année de publication : 2022 Collection : Abstracts of the ICA num. 4 Projets : LostInZoom / Touya, Guillaume Conférence : ICA 2021, 24th ICA Workshop on Map Generalisation and Multiple Representation 13/12/2021 13/12/2021 Florence Italie OA Proceedings Note générale : bibliographie
This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No. 101003012).Langues : Anglais (eng) Descripteur : [Termes IGN] objet géographique
[Termes IGN] réseau routier
[Termes IGN] simplification de contour
[Termes IGN] test de performance
[Vedettes matières IGN] GénéralisationRésumé : (auteur) [début] Road network selection is one of the major issues of map generalisation, as new papers are proposed every year since the first attempts of automation in the 1990’s (Thomson & Richardson, 1995). New methods are regularly proposed because selecting roads for maps at smaller scales is a complex problem. Roads are at the same time present in maps to enable car navigation tasks, and because they are structuring elements that reveal the nature of the landscape (urban, rural, mountainous…). So road selection is not only about retaining the most important roads of the network, but the preservation of topology and connectivity is essential, as well as the preservation, or the typification of road patterns (e.g. a ring road), and the preservation of local density differences (between urban and rural areas for instance). It is rare to see comparisons of road selection techniques in the literature, because of the lack of open source in map generalisation, but also because of the lack of a common dataset to benchmark these techniques; new propositions on road selection are most of the time tied to their own dataset and use case. This is why we think that this BasqueRoads dataset could be useful to advance on this topic of road network selection. Numéro de notice : C2021-066 Affiliation des auteurs : UGE-LASTIG (2020- ) Thématique : GEOMATIQUE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.5194/ica-abs-4-5-2022 Date de publication en ligne : 14/01/2022 En ligne : https://doi.org/10.5194/ica-abs-4-5-2022 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99536 A benchmark of named entity recognition approaches in historical documents : application to 19th century French directories / Nathalie Abadie (2022)
Titre : A benchmark of named entity recognition approaches in historical documents : application to 19th century French directories Type de document : Article/Communication Auteurs : Nathalie Abadie , Auteur ; Edwin Carlinet, Auteur ; Joseph Chazalon, Auteur ; Bertrand Duménieu , Auteur Editeur : Berlin, Heidelberg, Vienne, New York, ... : Springer Année de publication : 2022 Collection : Lecture notes in Computer Science, ISSN 0302-9743 num. 13237 Projets : SODUCO / Perret, Julien Conférence : DAS 2022, 5th IAPR International Workshop on Document Analysis Systems 22/05/2022 25/05/2022 La Rochelle France Proceedings Springer Importance : pp 445 - 460 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] dix-neuvième siècle
[Termes IGN] données d'entrainement (apprentissage automatique)
[Termes IGN] exploration de texte
[Termes IGN] objet géohistorique
[Termes IGN] reconnaissance de noms
[Termes IGN] traitement du langage naturelRésumé : (auteur) Named entity recognition (NER) is a necessary step in many pipelines targeting historical documents. Indeed, such natural language processing techniques identify which class each text token belongs to, e.g. “person name”, “location”, “number”. Introducing a new public dataset built from 19th century French directories, we first assess how noisy modern, off-the-shelf OCR are. Then, we compare modern CNN- and Transformer-based NER techniques which can be reasonably used in the context of historical document analysis. We measure their requirements in terms of training data, the effects of OCR noise on their performance, and show how Transformer-based NER can benefit from unsupervised pre-training and supervised fine-tuning on noisy data. Results can be reproduced using resources available at https://github.com/soduco/paper-ner-bench-das22 and https://zenodo.org/record/6394464. 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