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Identification of enclaves and exclaves by computation based on point-set topology / Xiaonan Wang in International journal of geographical information science IJGIS, vol 37 n° 2 (February 2023)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 37 n° 2 (February 2023) . - pp 307 - 338 Titre : Identification of enclaves and exclaves by computation based on point-set topology Type de document : Article/Communication Auteurs : Xiaonan Wang, Auteur Année de publication : 2023 Article en page(s) : pp 307 - 338 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] frontière [Termes IGN] géopolitique [Termes IGN] intersection spatiale [Termes IGN] partition de surface [Termes IGN] polygone [Termes IGN] relation topologique [Termes IGN] territoire [Termes IGN] topologie Résumé : (auteur) Enclaves and exclaves have special roles in geography and are of particular importance to fields such as (geo)politics and economy. However, enclaves and exclaves have not been defined with sufficient formality for automatic identification yet. To identify enclaves and exclaves more generally by computational means than current definitions existing in the literature, this article proposes expressive and generalized mathematical definitions of enclaves and exclaves based on point-set topology. A novel Boundary Extended 16-Intersection Model is developed in this article to identify enclaves, and 74 possible spatial configurations of enclaves are distinguished according to conditions of intersections for polygons in partitions and enclaves. The classic Dimensionally Extended 9-Intersection Model is employed to identify exclaves, and two possible spatial configurations of exclaves are distinguished according to conditions of intersections for polygons in partitions and exclaves. Applications of the proposed definitions are exemplified by the identification of enclaves and exclaves in prototypes and in the real world. Numéro de notice : A2023-102 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2022.2120995 Date de publication en ligne : 21/09/2022 En ligne : https://doi.org/10.1080/13658816.2022.2120995 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102428 [article]

Interpreting the fuzzy semantics of natural-language spatial relation terms with the fuzzy random forest algorithm / Xiaonan Wang in ISPRS International journal of geo-information, vol 7 n° 2 (February 2018)  

Public [article]  inISPRS International journal of geo-information > vol 7 n° 2 (February 2018) Titre : Interpreting the fuzzy semantics of natural-language spatial relation terms with the fuzzy random forest algorithm Type de document : Article/Communication Auteurs : Xiaonan Wang, Auteur ; Shihong Du, Auteur ; Chen-Chieh Feng, Auteur ; Xueying Zhang, Auteur ; Xiuyuan Zhang, Auteur Année de publication : 2018 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] langage naturel (informatique) [Termes IGN] relation sémantique [Termes IGN] relation topologique [Termes IGN] toponyme flou Résumé : (Auteur) Naïve Geography, intelligent geographical information systems (GIS), and spatial data mining especially from social media all rely on natural-language spatial relations (NLSR) terms to incorporate commonsense spatial knowledge into conventional GIS and to enhance the semantic interoperability of spatial information in social media data. Yet, the inherent fuzziness of NLSR terms makes them challenging to interpret. This study proposes to interpret the fuzzy semantics of NLSR terms using the fuzzy random forest (FRF) algorithm. Based on a large number of fuzzy samples acquired by transforming a set of crisp samples with the random forest algorithm, two FRF models with different membership assembling strategies are trained to obtain the fuzzy interpretation of three line-region geometric representations using 69 NLSR terms. Experimental results demonstrate that the two FRF models achieve good accuracy in interpreting line-region geometric representations using fuzzy NLSR terms. In addition, fuzzy classification of FRF can interpret the fuzzy semantics of NLSR terms more fully than their crisp counterparts. Numéro de notice : A2018-107 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi7020058 En ligne : https://doi.org/10.3390/ijgi7020058 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89533 [article]

Classifying natural-language spatial relation terms with random forest algorithm / Shihong Du in International journal of geographical information science IJGIS, vol 31 n° 3-4 (March-April 2017)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 31 n° 3-4 (March-April 2017) . - pp 542 - 568 Titre : Classifying natural-language spatial relation terms with random forest algorithm Type de document : Article/Communication Auteurs : Shihong Du, Auteur ; Xiaonan Wang, Auteur ; Chen-Chieh Feng, Auteur ; Xiuyuan Zhang, Auteur Année de publication : 2017 Article en page(s) : pp 542 - 568 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] apprentissage dirigé [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] intelligence artificielle [Termes IGN] interface en langage naturel [Termes IGN] langage naturel (informatique) [Termes IGN] méthode robuste [Termes IGN] recherche d'information géographique [Termes IGN] relation spatiale [Termes IGN] relation topologique [Termes IGN] similitude sémantique Résumé : (Auteur) The exponential growth of natural language text data in social media has contributed a rich data source for geographic information. However, incorporating such data source for GIS analysis faces tremendous challenges as existing GIS data tend to be geometry based while natural language text data tend to rely on natural language spatial relation (NLSR) terms. To alleviate this problem, one critical step is to translate geometric configurations into NLSR terms, but existing methods to date (e.g. mean value or decision tree algorithm) are insufficient to obtain a precise translation. This study addresses this issue by adopting the random forest (RF) algorithm to automatically learn a robust mapping model from a large number of samples and to evaluate the importance of each variable for each NLSR term. Because the semantic similarity of the collected terms reduces the classification accuracy, different grouping schemes of NLSR terms are used, with their influences on classification results being evaluated. The experiment results demonstrate that the learned model can accurately transform geometric configurations into NLSR terms, and that recognizing different groups of terms require different sets of variables. More importantly, the results of variable importance evaluation indicate that the importance of topology types determined by the 9-intersection model is weaker than metric variables in defining NLSR terms, which contrasts to the assertion of ‘topology matters, metric refines’ in existing studies. Numéro de notice : A2017-078 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2016.1212356 En ligne : http://dx.doi.org/10.1080/13658816.2016.1212356 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84340 [article]

Exemplaires(2)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
079-2017021	RAB	Revue	Centre de documentation	En réserve L003	Disponible
079-2017022	RAB	Revue	Centre de documentation	En réserve L003	Disponible