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. © 2017 The Author(s).
| Translated title of the contribution | Architecture and prototypical implementation of a semantic querying system for big Earth observation image bases |
|---|---|
| Original language | English |
| Pages (from-to) | 452-463 |
| Journal | European Journal of Remote Sensing |
| Volume | 50 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - 2017 |
Bibliographical note
Funding: Austrian Science Fund (DK W1237-N23), Österreichische Forschungsförderungsgesellschaft (848009, 855467)Keywords
- Array database
- Big data
- Earth observation
- Level 2 product
- Semantic content-based image retrieval
- Spatiotemporal objects and events
Fields of Science and Technology Classification 2012
- 105 Geosciences
- 207 Environmental Engineering, Applied Geosciences