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GazPNE: annotation-free deep learning for place name extraction from microblogs leveraging gazetteer and synthetic data by rules / Xuke Hu in International journal of geographical information science IJGIS, vol 36 n° 2 (February 2022)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 36 n° 2 (February 2022) . - pp 310 - 337 Titre : GazPNE: annotation-free deep learning for place name extraction from microblogs leveraging gazetteer and synthetic data by rules Type de document : Article/Communication Auteurs : Xuke Hu, Auteur ; Hussein S. Al-Olimat, Auteur ; Jens Kersten, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 310 - 337 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] apprentissage profond [Termes IGN] classification hybride [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] données topographiques [Termes IGN] extraction de données [Termes IGN] géobalise [Termes IGN] microblogue [Termes IGN] OpenStreetMap [Termes IGN] répertoire toponymique [Termes IGN] toponyme [Termes IGN] Twitter Résumé : (auteur) Extracting precise location information from microblogs is a crucial task in many applications, particularly in disaster response, revealing where damages are, where people need assistance, and where help can be found. A crucial prerequisite to location extraction is place name extraction. In this paper, we present GazPNE: a hybrid approach to place name extraction which fuses rules, gazetteers, and deep learning techniques without requiring any manually annotated data. The core of the approach is to learn the intrinsic characteristics of multi-word place names with deep learning from gazetteers. Specifically, GazPNE consists of a rule-based system to select n-grams from the microblogs that potentially contain place names, and a C-LSTM model that decides if the selected n-gram is a place name or not. The C-LSTM is trained on 388.1 million examples containing 6.8 million positive examples with US and Indian place names extracted from OpenStreetMap and 381.3 million negative examples synthesized by rules. We evaluate GazPNE against the SoTA on a manually annotated 4,500 tweet dataset which contains 9,026 place names from three foods: 2016 in Louisiana (US), 2016 in Houston (US), and 2015 in Chennai (India). GazPNE achieves SotA performance on the test data with an F1 of 0.84. Numéro de notice : A2022-164 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2021.1947507 Date de publication en ligne : 07/07/2021 En ligne : https://doi.org/10.1080/13658816.2021.1947507 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99787 [article]

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 d'arbres décisionnels [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]

APFiLoc: An Infrastructure-Free Indoor Localization method fusing smartphone inertial sensors, landmarks and map information / Jianga Shang in Sensors, vol 15 n° 10 (October 2015)  

Public [article]  inSensors > vol 15 n° 10 (October 2015) . - pp 27251 - 27272 Titre : APFiLoc: An Infrastructure-Free Indoor Localization method fusing smartphone inertial sensors, landmarks and map information Type de document : Article/Communication Auteurs : Jianga Shang, Auteur ; Fuqiang Gu, Auteur ; Xuke Hu, Auteur ; Allison Kealy, Auteur Année de publication : 2015 Article en page(s) : pp 27251 - 27272 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Navigation et positionnement [Termes IGN] analyse de groupement [Termes IGN] bâtiment [Termes IGN] centrale inertielle [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] information cartographique [Termes IGN] information complexe [Termes IGN] point de repère [Termes IGN] positionnement en intérieur [Termes IGN] téléphone intelligent Résumé : (auteur) The utility and adoption of indoor localization applications have been limited due to the complex nature of the physical environment combined with an increasing requirement for more robust localization performance. Existing solutions to this problem are either too expensive or too dependent on infrastructure such as Wi-Fi access points. To address this problem, we propose APFiLoc—a low cost, smartphone-based framework for indoor localization. The key idea behind this framework is to obtain landmarks within the environment and to use the augmented particle filter to fuse them with measurements from smartphone sensors and map information. A clustering method based on distance constraints is developed to detect organic landmarks in an unsupervised way, and the least square support vector machine is used to classify seed landmarks. A series of real-world experiments were conducted in complex environments including multiple floors and the results show APFiLoc can achieve 80% accuracy (phone in the hand) and around 70% accuracy (phone in the pocket) of the error less than 2 m error without the assistance of infrastructure like Wi-Fi access points. Numéro de notice : A2015--043 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : 10.3390/s151027251 En ligne : http://dx.doi.org/10.3390/s151027251 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=81864 [article]