Dataset Information

Architecture and prototypical implementation of a semantic querying system for big Earth observation image bases.

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.

SUBMITTER: Tiede D

PROVIDER: S-EPMC5632919 | biostudies-other | 2017

REPOSITORIES: biostudies-other

ACCESS DATA

Publications

Architecture and prototypical implementation of a semantic querying system for big Earth observation image bases.

Tiede Dirk D Baraldi Andrea A Sudmanns Martin M Belgiu Mariana M Lang Stefan S

European journal of remote sensing 20170811 1

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 c ...[more]

PMID: 29098143

Similar Datasets

Project description:BackgroundRoot is the principal part of plants to absorb water and nutrient, anchor the plant and affect yield and quality. Non-destructive detection of root traits is difficult to perform due to the hidden nature of the root. Therefore, improved methods to measure roots are necessary to support plant breeding, and optimization of cultivation and management. In this study, we present an adaptive minirhizotron along with installation patterns to focus on micro and local changes in multipoint of pepper roots.ResultsThe method is to improve minirhizotron by reducing its size to a microrhizotron (1.1?×?1.1?×?1.2 cm) and improving installation validity and rationality according to spatial distribution characteristics of Capsicum annuum root system. This adaptive minirhizotron could acquire root image in multipoint, and through image processing, root traits such as root length (including very fine roots or root hairs) and root width could be calculated. In order to install the microrhizotron reasonably and effectively, root system architecture (Capsicum annuum) was reconstructed using a three-dimensional caliper, and were quantified in circumferential distribution, vertical direction and root extension. The results showed that most lateral roots were constrained to 45° in horizontal direction to root initial position; Vertical angles were large, almost perpendicular to the root center line at initial position, and it became smaller when roots began to deepen. Root length density decreased with the increase of distance to plant center. According to Capsicum annuum root system traits, totally 8 installation methods were tested and verified to determine high probability of root interception. Horizontal angle 45° showed much higher interception probability than that of 90°. Vertical angle 45° has slightly higher root interception probability than that of 30°. Installation pattern horizontal angle 45°?+?radius 30 mm?+?vertical angle 45° showed the best performance in root interception with probability of 96.7%, followed by pattern horizontal angle 45°?+?radius 30 mm?+?vertical angle 30°. Comparison experiment showed that when root hair and very fine root were excluded, relative error was 12.1% between microrhizotron and soil sampling in root length, and 15.4% in root diameter. Microrhizotron was able to observe fine roots about 0.1 mm in diameter.ConclusionA new adaptive minirhizotron has been established for nondestructive observation on local and micro changes of roots in multipoint, and its application and installation patterns has been suggested according to root architecture traits. The microrhizotron can be used to study a wide range of research questions focused on quantitative trait locus analysis, root width changes, and root hair growth.

Dataset Information

Architecture and prototypical implementation of a semantic querying system for big Earth observation image bases.

Publications

Architecture and prototypical implementation of a semantic querying system for big Earth observation image bases.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets