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Room semantics inference using random forest and relational graph convolutional networks: A case study of research building / Xuke Hu in Transactions in GIS, Vol 25 n° 1 (February 2021)  

Public [article]  inTransactions in GIS > Vol 25 n° 1 (February 2021) . - pp 71 - 111 Titre : Room semantics inference using random forest and relational graph convolutional networks: A case study of research building Type de document : Article/Communication Auteurs : Xuke Hu, Auteur ; Hongchao Fan, Auteur ; Alexey Noskov, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 71 - 111 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] apprentissage automatique [Termes IGN] bâtiment public [Termes IGN] carte d'intérieur [Termes IGN] cartographie automatique [Termes IGN] classification par forêts aléatoires [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] graphe relationnel [Termes IGN] inférence sémantique Résumé : (Auteur) Semantically rich maps are the foundation of indoor location‐based services. Many map providers such as OpenStreetMap and automatic mapping solutions focus on the representation and detection of geometric information (e.g., shape of room) and a few semantics (e.g., stairs and furniture) but neglect room usage. To mitigate the issue, this work proposes a general room tagging method for public buildings, which can benefit both existing map providers and automatic mapping solutions by inferring the missing room usage based on indoor geometric maps. Two kinds of statistical learning‐based room tagging methods are adopted: traditional machine learning (e.g., random forests) and deep learning, specifically relational graph convolutional networks (R‐GCNs), based on the geometric properties (e.g., area), topological relationships (e.g., adjacency and inclusion), and spatial distribution characteristics of rooms. In the machine learning‐based approach, a bidirectional beam search strategy is proposed to deal with the issue that the tag of a room depends on the tag of its neighbors in an undirected room sequence. In the R‐GCN‐based approach, useful properties of neighboring nodes (rooms) in the graph are automatically gathered to classify the nodes. Research buildings are taken as examples to evaluate the proposed approaches based on 130 floor plans with 3,330 rooms by using fivefold cross‐validation. The experiments conducted show that the random forest‐based approach achieves a higher tagging accuracy (0.85) than R‐GCN (0.79). Numéro de notice : A2021-186 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12664 date de publication en ligne : 19/08/2020 En ligne : https://doi.org/10.1111/tgis.12664 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97152 [article]

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 IGN] appariement d'adresses [Termes IGN] appariement sémantique [Termes IGN] apprentissage automatique [Termes IGN] apprentissage profond [Termes IGN] géocodage par adresse postale [Termes IGN] gestion urbaine [Termes IGN] inférence sémantique [Termes IGN] repésentation vectorielle [Termes IGN] réseau neuronal profond [Termes IGN] Shenzhen [Termes IGN] similitude sémantique [Termes 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 IGN] image aérienne [Termes IGN] indexation sémantique [Termes IGN] inférence sémantique [Termes 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 IGN] croisement spatial [Termes IGN] inférence sémantique [Termes IGN] intégration de données [Termes IGN] ontologie [Termes 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 IGN] cohérence des données [Termes IGN] données hétérogènes [Termes IGN] données localisées des bénévoles [Termes IGN] inférence sémantique [Termes IGN] niveau de détail [Termes IGN] OpenStreetMap [Termes 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)

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