Semantic and syntactic interoperability in online processing of big Earth observation data. Issue 1 (2nd January 2018)

• Record Type: Journal Article
• Authors: Sudmanns, Martin; Tiede, Dirk; Lang, Stefan; Baraldi, Andrea
• Is Part Of: International journal of digital earth. Volume 11:Issue 1(2018)
• Page Start: 95
• Page End: 112
• Publication Date: 2018-01-02
• Subjects: OGC WPS -- big Earth data -- time series -- large-scale processing -- model-driven semantic analyses; Geographic information systems -- Periodicals; Sustainable development -- Information technology -- Periodicals; Social planning -- Information technology -- Periodicals; 910.285
• DOI: 10.1080/17538947.2017.1332112
• Languages: English
• ISSNs: 1753-8947

Abstract

ABSTRACT: The challenge of enabling syntactic and semantic interoperability for comprehensive and reproducible online processing of big Earth observation (EO) data is still unsolved. Supporting both types of interoperability is one of the requirements to efficiently extract valuable information from the large amount of available multi-temporal gridded data sets. The proposed system wraps world models, (semantic interoperability) into OGC Web Processing Services (syntactic interoperability) for semantic online analyses. World models describe spatio-temporal entities and their relationships in a formal way. The proposed system serves as enabler for (1) technical interoperability using a standardised interface to be used by all types of clients and (2) allowing experts from different domains to develop complex analyses together as collaborative effort. Users are connecting the world models online to the data, which are maintained in a centralised storage as 3D spatio-temporal data cubes. It allows also non-experts to extract valuable information from EO data because data management, low-level interactions or specific software issues can be ignored. We discuss the concept of the proposed system, provide a technical implementation example and describe three use cases for extracting changes from EO images and demonstrate the usability also for non-EO, gridded, multi-temporal data sets (CORINE land cover).