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Exploration of OpenStreetMap missing built-up areas using twitter hierarchical clustering and deep learning in Mozambique / Hao Li in ISPRS Journal of photogrammetry and remote sensing, vol 166 (August 2020)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 166 (August 2020) . - pp 41-51 Titre : Exploration of OpenStreetMap missing built-up areas using twitter hierarchical clustering and deep learning in Mozambique Type de document : Article/Communication Auteurs : Hao Li, Auteur ; Benjamin Herfort, Auteur ; Wei Huang, Auteur Année de publication : 2020 Article en page(s) : pp 41-51 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web [Termes descripteurs IGN] analyse de groupement [Termes descripteurs IGN] analyse spatiale [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] carte sanitaire [Termes descripteurs IGN] cartographie collaborative [Termes descripteurs IGN] données localisées des bénévoles [Termes descripteurs IGN] géographie sociale [Termes descripteurs IGN] inventaire du bâti [Termes descripteurs IGN] Mozambique [Termes descripteurs IGN] OpenStreetMap [Termes descripteurs IGN] qualité des données [Termes descripteurs IGN] Twitter Résumé : (auteur) Accurate and detailed geographical information digitizing human activity patterns plays an essential role in response to natural disasters. Volunteered geographical information, in particular OpenStreetMap (OSM), shows great potential in providing the knowledge of human settlements to support humanitarian aid, while the availability and quality of OSM remains a major concern. The majority of existing works in assessing OSM data quality focus on either extrinsic or intrinsic analysis, which is insufficient to fulfill the humanitarian mapping scenario to a certain degree. This paper aims to explore OSM missing built-up areas from an integrative perspective of social sensing and remote sensing. First, applying hierarchical DBSCAN clustering algorithm, the clusters of geo-tagged tweets are generated as proxies of human active regions. Then a deep learning based model fine-tuned on existing OSM data is proposed to further map the missing built-up areas. Hit by Cyclone Idai and Kenneth in 2019, the Republic of Mozambique is selected as the study area to evaluate the proposed method at a national scale. As a result, 13 OSM missing built-up areas are identified and mapped with an over 90% overall accuracy, being competitive compared to state-of-the-art products, which confirms the effectiveness of the proposed method. Numéro de notice : A2020-350 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.05.007 date de publication en ligne : 07/06/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.05.007 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95233 [article]

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A hybrid classification matching method for geospatial services / Yandong Wang in Transactions in GIS, vol 16 n° 6 (December 2012)  

Public [article]  inTransactions in GIS > vol 16 n° 6 (December 2012) . - pp 781 - 805 Titre : A hybrid classification matching method for geospatial services Type de document : Article/Communication Auteurs : Yandong Wang, Auteur ; Hao Li, Auteur ; An Luo, Auteur Année de publication : 2012 Article en page(s) : pp 781 - 805 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web [Termes descripteurs IGN] classification hybride [Termes descripteurs IGN] service web géographique [Termes descripteurs IGN] système d'information géographique [Termes descripteurs IGN] taxinomie Résumé : (Auteur) With the development of the Internet and GIS, large volumes of spatial data and powerful computing resources are increasingly published in the form of Web services. Given the variety and number of geospatial services advertised online, finding appropriate geospatial services has become a tremendous challenge for potential users. Geospatial service classification provides a basis for developing matching criteria to improve the efficiency of service discovery. At present, most classification-based matching methods require users to provide classification descriptions using a specified taxonomy. These requirements seriously limit the application of classification-based matching. To solve these kinds of problems, this article presents a hybrid geospatial service classification-matching method. Based on the differences in classification descriptions, three strategies are proposed: (1) the existing classification matching method is used for requests with classifications described using homogeneous taxonomies; (2) a formal-concept-analysis-based service classification matching method is proposed for service requests with classifications described using heterogeneous taxonomies; and (3) an interface-similarity-based service classification decision method is proposed for requests without classification descriptions. The feasibility of the hybrid geospatial service classification matching method is verified by two sets of experiments. The results reveal that this method can effectively broaden the application of classification-based matching in geospatial service discovery. Numéro de notice : A2012-616 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/j.1467-9671.2012.01348.x En ligne : https://doi.org/10.1111/j.1467-9671.2012.01348.x Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32062 [article]