Lessons learned in processing, combining, and

disseminating in-situ and Copernicus EO data

in a spatial data infrastructure

Prof. Dr. Albert Remke, 52°North

Arne de Wall, 52° North

Christian Knoth, Institute for Geoinformatics – University of Münster, Germany

Dr. Thore Fechner, con terra GmbH

Question What are the challenges in the integration and

processing of earth observation data from the

Copernicus programme in a spatial data

infrastructure in combination with in-situ data?

Approach Explore the challenges with senior master students

• M.Sc. Geoinformatics

• M.Sc. Geospatial Technologies

• Comprehensive understanding of the Copernicus Earth

Observation (EO) infrastructure

• Improved understanding of the technology stack for

building such infrastructures (cloud, image analysis &

geoprocessing, web services, web apps)

• Improved software engineering skills such as agile

software development, communication and collaboration

in a team of software engineers, self-organization

Educational Goals

Challenge for 15 master students

Develop a flood relief management application based on

the Copernicus EO infrastructure

• Providing subsets of Sentinel-1 data in the cloud

• Providing image layers with detected waterbodies

• Providing a web application enriched with in-situ data

• Limiting the extent of the view to the region of

North Rhine-Westphalia (NRW)

Study project @ Institute for Geoinformatics

Requirements (by the students)

• Automated dynamic mirroring of Sentinel-1 data into the

datahub beginning 1.1.2016, covering the region of NRW

• Automatic processing of the satellite imagery for water

detection

• Automatic integration of in-situ data (water levels, weather)

• Near real-time visualization of gauging stations, precipitation

and weather

• Navigating through the information products by

time and space

Ingestion Processing StorageDistribution &

ExplorationApplications

Three student teams:

Team 1:

Infrastructure & Ingestion

Team 2:

Processing & Detection

Team 3:

Services & Web App

Data

• Sentinel-1 as it is weather independent

• Gauging stations from Pegelonline.de

• Weather forecast from Open Weather Map

Infrastructure

• Amazon Web Services

• ESRI Technology Stack

Data and Infrastructure for the flood relief management application

Logical components

and data flow

Copernicus

• Orbit files are not always present (but can be located)

• Data is repacked and organized differently (beginning vs. now)

• Ingestion scripts need a high resilience and strong error reporting

In-situ data

• Matching different time resolutions requires more work than expected

• It is benefical to ingest in-situ data into the own infrastructure

Infastructure

• Nodes in „the cloud“ can have different software versions (Frankfurt vs. Ireland)

Identified Challenges / Lessons Learned

Results: Requirements (by the students)

• Automated dynamic mirroring of Sentinel-1 data into the

datahub beginning 1.1.2016, covering the region of NRW

• Automatic processing of the satellite imagery for water

detection

• Automatic integration of in-situ data (water levels, weather)

• Near real-time visualization gauging stations, precipitation

and weather

• Navigating through the information products by

time and space

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Dr. Thore Fechner

con terra – Gesellschaft für Angewandte Informationstechnologie mbH

Martin-Luther-King-Weg 24

48155 Münster

Telefon +49 89 207 005 2200

info@conterra.de