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SUCE

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SUCE (Suitability Coverage Engine) is an 18-months project which started in March 2015.

The SUCE Engine to be developed aims at providing users with the ability to perform suitability analysis on archived datasets based on metadata, return suitable results (in a form of metadata including maps), analyse potential gaps and allow modifying the criteria, and use the metadata to directly download the dataset needed for defined mapping product from the archive(s).

Project website: suce.gisat.cz

 

OBJECTIVES

The principal objective of the SUCE project is to define a concept and architecture, and provide a prototype permitting to effectively download EO products from identified PDGS (Payload Data Ground Segment) on the basis of advanced user criteria and analytic needs.

The aim is to retrieve optimal EO image products sets suitable for the defined user activities for mapping and monitoring tasks (e.g. European Copernicus (GMES)), avoiding both manual filtering and transfer of useless data.

 

PROJECT

The project is divided into the following subsequent parts:

Scenarios and requirements definition

-End-to-end use case scenarios and associated requirements are being identified in order to define SUCE architecture at system level and the software requirements and architecture engineering at subsystem level. There are four generic requirements: 1) Single image retrieval; 2) Seamless spatial coverage; 3) Temporal coverage and 4) the combination of 2) and 3).

EO product metadata analysis

-Each of the repositories established by the Earth Observation (EO) data providers permitting to access and download selected satellite imagery are being identified and analysed.

Prototype design- implementation, verification and deployment of the system architecture

-This part represents the core development activity of the project and it aims at providing a scalable and modular SUCE Engine. The entire software development process follows an open source approach.

Prototype validation and evaluation

-Three main directions are set to ensure the prototype validation and evaluation: validation against use cases, validation based on simple use cases built on top of simulated metadata and validation involving end-users.

VAPs algorithms implementation

-The algorithms proposed to detect clouds, snow and cloud shadows, and algorithms proposed to generate the classification mask will be delivered together with the quality indicators. 

 

Project member resources


Contributors to this page: Marketa Jindrova
RSS Team
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Page last modified on Monday 11 of May 2015 10:56:58 CEST by Marketa Jindrova.