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Lessons learned in processing, combining, and disseminating in-situ and Copernicus EO data in a spatial data infrastructure

Thore Fechner, Christian Knoth, Arne de Wall and Albert Remke

(Submission #260)


What are the challenges in the integration and processing of earth observation data from the Copernicus program in a spatial data infrastructure in combination with in-situ data?

We posed this question to a course of senior master students in Geoinformatics at the Institute for Geoinformatics, University of M√ľnster, Germany. Together with the students, we tackled the challenge of establishing a cloud-based spatial data infrastructure that ingests, processes and combines the EO data with data from in-situ sources. The resulting data is then disseminated using standard geospatial interfaces and consumed by a simple proof-of-concept geo-visualization. Our concrete task was to continuously and automatically identify open water bodies in the federal state of North Rhine-Westphalia (NRW) using Sentinel 1 data for a disaster management scenario. Additionally, we included real-time in-situ data in the processing chain and visualization component, e.g., current water levels or predicted precipitation alongside other static sources such as a high-resolution digital elevation model that is published as Open Data by NRW.

Attendees of this talk can expect an open and honest report and dialog about what worked, where we failed, and what we did or would do about it. The talk will focus on the bigger picture of integrating and processing Copernicus data in existing spatial data infrastructures and will not delve into minute technical details. Instead, it concentrates on the lessons learned, the training of the students, and the role spatial data infrastructures can play in the context of potentially vast amounts of EO data.


Topic Area:  [2.8] Innovation and technologies for spatial data collection, processing and integration in spatial data infrastructures (for example; Galileo/EGNOSS, Copernicus data and services, sensor web, Internet of Things, Big Data analytics)
Abstract Type:  Oral Presentation

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