The software architecture is composed by a web application (BIS client) and a web service (BIS service). The former can be accessed using a common browser and presents in a user-friendly way the core functionalities supplied by the latter. Underneath, the two components interact through the means of an HTTP API based on the LCML-based data model; the BIS service makes use of a geo-database to store georeferenced data and a native XML database to store and transform the LCML collections according to the user requests. The LCML-based data model is encoded using XML schema, in order to leverage the query capabilities of a native XML database (e.g. XQuery, XPath). The use of native XML technologies seems a reasonable choice to enable the system scaling, maintaining unaltered the full set of information that is available in LCCS3/LCML. Future steps of this work include the application of BIS functionalities on even larger LCCS3 legends (e.g. output of automatic classification systems).

An Object-Oriented Method to Assess Semantic Similarity between LCML based Legends

2017

Abstract

The software architecture is composed by a web application (BIS client) and a web service (BIS service). The former can be accessed using a common browser and presents in a user-friendly way the core functionalities supplied by the latter. Underneath, the two components interact through the means of an HTTP API based on the LCML-based data model; the BIS service makes use of a geo-database to store georeferenced data and a native XML database to store and transform the LCML collections according to the user requests. The LCML-based data model is encoded using XML schema, in order to leverage the query capabilities of a native XML database (e.g. XQuery, XPath). The use of native XML technologies seems a reasonable choice to enable the system scaling, maintaining unaltered the full set of information that is available in LCCS3/LCML. Future steps of this work include the application of BIS functionalities on even larger LCCS3 legends (e.g. output of automatic classification systems).
2017
Inglese
WorldCover 2017
https://www.conftool.pro/worldcover2017/index.php?page=browseSessions&print=head&form_session=12#paperID116
14/03/2017
land cover classification
lcml
lccs3
none
info:eu-repo/semantics/conferenceObject
Antonio Di GregorioNicola MoscaEnrico BoldriniSimone MaffeiPalma Blonda
275
04 Contributo in convegno::04.03 Poster in Atti di convegno
3
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/331574
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