The document discusses the importance of data interoperability in environmental data applications, highlighting issues such as data fragmentation across technologies, quality, and semantics. It emphasizes the need for improved environmental monitoring and accessible information to address complex environmental problems, proposing a cloud-based data processing platform and citizen science to enhance data utilization. The seminar agenda outlines various sessions focused on data sources, user engagement, and quality control in environmental monitoring.

1. 1
Introduction to data interoperability
Environmental data for applications Seminar
23 Sept, 2015
Ville Kotovirta
VTT Technical Research Centre of Finland Ltd

2. 2
Satellite
Radar
In-situ
Modelling
Citizen science
Research

3. 3
201519801950
Amount of
environmental
data
Amount of
interoperable
data
Amount of
data in use
201519801950
Amount of
environmental
data
Amount of
interoperable
data
Amount of
data in use
Big data
gap
Interoperability
gap

4. 4
Currently visible data value
of known sources and uses
Potential data value -
currently hidden because
data are not known and
cannot be accessed
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Data fragmentation
Fragmentation in technology
Different sensors are based on different technology, different
data formats are used
Fragmentation in data quality
Various data quality requirements
Fragmentation in space and time
Different sensors and models have different spatial and temporal
resolutions
Fragmentation in semantics
Different organisations, different countries, different disciplines
use different semantics for data
Fragmentation in data politics
differing views on how the data should be shared
The need for interoperability is not taken into account when systems
are designed and built
As a result, it is too expensive for users to use the data

5. 5
0
5000
10000
15000
20000
25000
30000
35000
40000
45000
1880 1900 1920 1940 1960 1980 2000 2020
Gross World Product (GWD) vs. CO2
Real GWP ($ billions)
CO2 emissions
(Teragrams)
https://en.wikipedia.org/wiki/Gross_world_product https://en.wikipedia.org/wiki/Global_warming#/media/File:Tr
endsGlobalEmissions.png

6. 6
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Anatomy of environmental problems
Environmental externalities – producers or consumers have
unintended external (indirect) effects on other producers or/and
consumers
Consumers cannot take environmental consequences of complex
production systems into account
reliable and understandable information does not exist
information cannot be accessed and valuated when choices
are made
More powerful environmental monitoring and information
processing are needed to solve environmental problems!
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Interoperability
Managing,
processing,
modelling
Tailoring
Challenges of environmental monitoring
Measurement QC
Data
value
Representation Business

7. 7
• Data inventory
• OGC, Inspire, API’s
• New sensor technology
• Sensor network control
• Citizen science
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MMEA (Measurement, Monitoring and Environmental Assessment)
to tackle the environmental monitoring challenges
• Cloud-based data
processing
platform
• QC of the whole
processing chain
• Fusion modelling
• Anomaly
detection
• Semantic
technologies
• Visualization
• Energy
production
• Water
management
• Agriculture
• Air quality
• Mining
• Data Operator
MMEA:
Interoperability
Managing,
processing,
modelling
TailoringMeasurement QC Representation Business
1424/09/2015
08:30 Coffee
09:00 Opening and welcome, Heikki Turtiainen, Vaisala
09:10 Introduction to environmental data interoperability, Ville Kotovirta, VTT
09:30 Session 1: Data, data everywhere - open and closed data Leader: Jari Silander, Syke
09:30 Available data sources, Jari Silander, Syke
09:50 Controlling environment monitoring networks, Olli Ojanperä & Panu Kilponen, Vaisala
10:30 Break
10:45 Engaging citizens - participatory sensing, Renne Tergujeff, VTT
11:10 Combining various data sources, Outi Mäyrä, University of Oulu
11:30 Commercialization of environmental big data, Anas Al Natsheh, CEMIS
11:50 Discussion
12:00 Lunch break (hosted)
13:00 Session 2: Connecting data and users Leader: Mikko Ala-Fossi, Vaisala
13:05 MMEA platform development, Harri Hytönen, Vaisala
13:20 Quality control and measurement uncertainty, Mauno Rönkkö, UEF
13:40 Variogram-derived measures for QC purposes, Markku Ohenoja, University of Oulu
14:00 Coffee break
14:30 Combining Two Datasets into a Single Map Animation, Salla Multimäki, Aalto
14:50 Visualization of coastline flooding, Janne Kovanen, MML
15:10 Applying data - case agriculture, Janne Saarela, Profium
15:45 Missing link in evolution - data operator for efficient use of data, Ville Kotovirta, VTT
16:00 Discussion
16:15 Closing
Agenda