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A deep learning architecture for semantic address matching / Yue Lin in International journal of geographical information science IJGIS, vol 34 n° 3 (March 2020)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 34 n° 3 (March 2020) . - pp 559 - 576 Titre : A deep learning architecture for semantic address matching Type de document : Article/Communication Auteurs : Yue Lin, Auteur ; Mengjun Kang, Auteur ; Yuyang Wu, Auteur Année de publication : 2020 Article en page(s) : pp 559 - 576 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes descripteurs IGN] appariement d'adresses [Termes descripteurs IGN] appariement sémantique [Termes descripteurs IGN] apprentissage automatique [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] géocodage par adresse postale [Termes descripteurs IGN] gestion urbaine [Termes descripteurs IGN] inférence sémantique [Termes descripteurs IGN] repésentation vectorielle [Termes descripteurs IGN] réseau de neurones profond [Termes descripteurs IGN] Shenzhen [Termes descripteurs IGN] similitude sémantique [Termes descripteurs IGN] traitement du langage naturel Résumé : (auteur) Address matching is a crucial step in geocoding, which plays an important role in urban planning and management. To date, the unprecedented development of location-based services has generated a large amount of unstructured address data. Traditional address matching methods mainly focus on the literal similarity of address records and are therefore not applicable to the unstructured address data. In this study, we introduce an address matching method based on deep learning to identify the semantic similarity between address records. First, we train the word2vec model to transform the address records into their corresponding vector representations. Next, we apply the enhanced sequential inference model (ESIM), a deep text-matching model, to make local and global inferences to determine if two addresses match. To evaluate the accuracy of the proposed method, we fine-tune the model with real-world address data from the Shenzhen Address Database and compare the outputs with those of several popular address matching methods. The results indicate that the proposed method achieves a higher matching accuracy for unstructured address records, with its precision, recall, and F1 score (i.e., the harmonic mean of precision and recall) reaching 0.97 on the test set. Numéro de notice : A2020-106 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2019.1681431 date de publication en ligne : 24/10/2019 En ligne : https://doi.org/10.1080/13658816.2019.1681431 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94702 [article]

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High-resolution aerial image labeling with convolutional neural networks / Emmanuel Maggiori in IEEE Transactions on geoscience and remote sensing, vol 55 n° 12 (December 2017)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 55 n° 12 (December 2017) . - pp 7092 - 7103 Titre : High-resolution aerial image labeling with convolutional neural networks Type de document : Article/Communication Auteurs : Emmanuel Maggiori, Auteur ; Yuliya Tarabalka, Auteur ; Guillaume Charpiat, Auteur ; Pierre Alliez, Auteur Année de publication : 2017 Article en page(s) : pp 7092 - 7103 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes descripteurs IGN] image aérienne [Termes descripteurs IGN] indexation sémantique [Termes descripteurs IGN] inférence sémantique [Termes descripteurs IGN] réseau neuronal convolutif Résumé : (Auteur) The problem of dense semantic labeling consists in assigning semantic labels to every pixel in an image. In the context of aerial image analysis, it is particularly important to yield high-resolution outputs. In order to use convolutional neural networks (CNNs) for this task, it is required to design new specific architectures to provide fine-grained classification maps. Many dense semantic labeling CNNs have been recently proposed. Our first contribution is an in-depth analysis of these architectures. We establish the desired properties of an ideal semantic labeling CNN, and assess how those methods stand with regard to these properties. We observe that even though they provide competitive results, these CNNs often underexploit properties of semantic labeling that could lead to more effective and efficient architectures. Out of these observations, we then derive a CNN framework specifically adapted to the semantic labeling problem. In addition to learning features at different resolutions, it learns how to combine these features. By integrating local and global information in an efficient and flexible manner, it outperforms previous techniques. We evaluate the proposed framework and compare it with state-of-the-art architectures on public benchmarks of high-resolution aerial image labeling. Numéro de notice : A2017-769 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2017.2740362 En ligne : https://doi.org/10.1109/TGRS.2017.2740362 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=88808 [article]

A semi-automatic lightweight ontology bridging for the semantic integration of cross-domain geospatial information / Jung-Hong Hong in International journal of geographical information science IJGIS, vol 29 n° 12 (December 2015)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 29 n° 12 (December 2015) . - pp 2223 - 2247 Titre : A semi-automatic lightweight ontology bridging for the semantic integration of cross-domain geospatial information Type de document : Article/Communication Auteurs : Jung-Hong Hong, Auteur ; Chiao-Ling Kuo, Auteur Année de publication : 2015 Article en page(s) : pp 2223 - 2247 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Systèmes d'information géographique [Termes descripteurs IGN] croisement spatial [Termes descripteurs IGN] inférence sémantique [Termes descripteurs IGN] intégration de données [Termes descripteurs IGN] ontologie [Termes descripteurs IGN] relation sémantique Résumé : (Auteur) Semantic integration has gained considerable attention in geographic information system (GIS) interoperability with the goal of conquering semantic heterogeneity. Over the past two decades, ontology engineering has been widely used to resolve semantic integration obstacles. However, most of the previous ontology integration approaches mainly focused on the same type of data with formal ontology. A key challenge is to identify the semantics of cross-domain data generated by different domains from their own perspectives. Given that formal ontology is not always available, this paper proposes a novel semi-automatic approach to determine the semantic relationship of cross-domain concepts based on lightweight ontologies. The semantics of each concept is presented by an extendable and structural definition framework composed of a number of resource description framework triple statements formed by common vocabularies extracted from the free-text definition of the concept. We further propose algorithms to compare the structural definition of concepts and to determine the semantic relationship of concepts between different domains. Finally, the semantic relationship of concepts from different domains is formally presented by the bridge ontology to serve future application needs. The bridge ontology of two GIS-based data, topographic map data and land use data, was developed to demonstrate the feasibility of the proposed approach. The preliminary result shows a faithful and highly automatic semantic integration for cross-domain data. Numéro de notice : A2015-623 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2015.1072200 En ligne : https://doi.org/10.1080/13658816.2015.1072200 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=78092 [article]

Detecting level-of-detail inconsistencies in volunteered geographic information data sets / Guillaume Touya in Cartographica, vol 48 n° 2 (June 2013)  

Public [article]  inCartographica > vol 48 n° 2 (June 2013) . - pp 134 - 143 Titre : Detecting level-of-detail inconsistencies in volunteered geographic information data sets Type de document : Article/Communication Auteurs : Guillaume Touya , Auteur ; Carmen Brando-Escobar, Auteur Congrès : ICC 2013, 26th International Cartographic Conference ICA (25 - 30 août 2013; Dresde, Allemagne), Commanditaire Année de publication : 2013 Article en page(s) : pp 134 - 143 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web [Termes descripteurs IGN] cohérence des données [Termes descripteurs IGN] données hétérogènes [Termes descripteurs IGN] données localisées des bénévoles [Termes descripteurs IGN] inférence sémantique [Termes descripteurs IGN] niveau de détail [Termes descripteurs IGN] OpenStreetMap [Termes descripteurs IGN] visualisation cartographique Mots-clés libres : cartography  lod  vgi Résumé : (Auteur) Whereas defining the level of detail (LoD) of authoritative data sets is possible, the opposite is true for volunteered geographic information (VGI), which is often characterized by heterogeneous LoDs. This heterogeneity is a curb for mapmaking, particularly when using traditional map derivation processes such as generalization. This paper proposes a method to infer the LoD of VGI features. Then, inconsistencies between features with different LoDs that get in the way of good mapmaking can be automatically identified. Some proposals are made to harmonize LoD heterogeneities. The inferring of LoDs is implemented, and results are presented on OpenStreetMap data. Numéro de notice : A2013-406 Affiliation des auteurs : IGN+Ext (2012-2019) Thématique : GEOMATIQUE/SOCIETE NUMERIQUE Nature : Article DOI : 10.3138/carto.48.2.1836 En ligne : http://www.utpjournals.press/doi/full/10.3138/carto.48.2.1836 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32544 [article]

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Semantic-sensitive satellite image retrieval / Y. Li in IEEE Transactions on geoscience and remote sensing, vol 45 n° 4 (April 2007)

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 45 n° 4 (April 2007) . - pp 853 - 860 Titre : Semantic-sensitive satellite image retrieval Type de document : Article/Communication Auteurs : Y. Li, Auteur ; T.R. Bretschneider, Auteur Année de publication : 2007 Article en page(s) : pp 853 - 860 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées [Termes descripteurs IGN] base de données d'images [Termes descripteurs IGN] image satellite [Termes descripteurs IGN] inférence sémantique [Termes descripteurs IGN] recherche d'information [Termes descripteurs IGN] réseau bayesien Résumé : (Auteur) Content-based image-retrieval techniques based on query scenes are a powerful means for exploration and mining of large remote sensing image databases. However, the gap between low-level unsupervised extracted features in content-based retrieval and the high-level semantic concepts of user queries limits the performance. Therefore, this paper proposes a specialized approach using a context-sensitive Bayesian network for semantic inference of segmented scenes. The regions' remote sensing related semantic concepts are inferred in a multistage process based on their spectral and textural characteristics as well as the semantics of adjacent regions. During the actual retrieval, the semantics are employed for the extraction of candidate scenes which are evaluated and ranked in a consecutive step. The approach was implemented and compared with a different strategy that utilizes the extracted features from the imagery directly to infer the semantics. In summary, the developed system achieved higher precision and recall rates using the same training data Copyright IEEE Numéro de notice : A2007-219 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28582 [article]

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