lessons learned in processing, combining, and ... · •automated dynamic mirroring of sentinel-1...
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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