Architecture and prototypical implementation of a semantic querying system for big Earth observation image bases. Issue 1 (1st January 2017)

• Record Type: Journal Article
• Authors: Tiede, Dirk; Baraldi, Andrea; Sudmanns, Martin; Belgiu, Mariana; Lang, Stefan
• Is Part Of: European journal of remote sensing. Volume 50:Issue 1(2017)
• Page Start: 452
• Page End: 463
• Publication Date: 2017-01-01
• Subjects: Big data -- Earth observation -- Level 2 product -- spatiotemporal objects and events -- array database -- semantic content-based image retrieval; Remote sensing -- Periodicals; Remote sensing; Electronic journals; Periodicals; 621.3678
• DOI: 10.1080/22797254.2017.1357432
• Languages: English
• ISSNs: 2279-7254

Abstract

ABSTRACT: Spatiotemporal analytics of multi-source Earth observation (EO) big data is a pre-condition for semantic content-based image retrieval (SCBIR). As a proof of concept, an innovative EO semantic querying (EO-SQ) subsystem was designed and prototypically implemented in series with an EO image understanding (EO-IU) subsystem. The EO-IU subsystem is automatically generating ESA Level 2 products (scene classification map, up to basic land cover units) from optical satellite data. The EO-SQ subsystem comprises a graphical user interface (GUI) and an array database embedded in a client server model. In the array database, all EO images are stored as a space-time data cube together with their Level 2 products generated by the EO-IU subsystem. The GUI allows users to (a) develop a conceptual world model based on a graphically supported query pipeline as a combination of spatial and temporal operators and/or standard algorithms and (b) create, save and share within the client-server architecture complex semantic queries/decision rules, suitable for SCBIR and/or spatiotemporal EO image analytics, consistent with the conceptual world model.