Jonas Eberle 25th March 2015 1

Automatization of information extractionto build up a crowd-sourced reference

database for vegetation changes

Jonas Eberle, Dr. Christian Hüttich, Prof. Christine Schmullius

Friedrich-Schiller-University Jena, GermanyInstitute for Geography, Department for Earth Observation

www.eo.uni-jena.de

Joint Breakout Session 1: Intelligent use of data quantity vs focusing on data quality

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Lots of data is freely available

Jonas Eberle 25th March 2015 3

Lots of data is freely available

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How to process available data?

For any dataset For different data formats For any time stamp

1. Find download files2. Download data3. Convert data4. Clip data to study area5. Apply quality masks

1. Find download files2. Download data3. Convert data4. Clip data to study area5. Apply quality masks

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Intelligent use of data quantity

• What is needed?1. Automation in data access

2. Easy to use clients

3. Possibility to create / publish new information

• What should be achieved in our case?– Try to explain a change detected in the time series Evaluate changes based on additional data

– Easy use of a wide range of datasets (no processing needed) Interactive change evaluation of spatial time-series data

Create new knowledge and information

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Intelligent use of data quantity

1. Automation in data access– Data Processing

Middleware

– Standardized data publishing

• OGC Web Services

– Necessary inputs• Name of dataset• Geometry (Point,

Polygon)• Further optional

parametersFigure: Multi-Source Data Processing Middleware

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Intelligent use of data quantity

2. Easy to use clients

– Web Portal• Modern web technologies• Based on web services• User accounts

– Mobile Application• Data access & analysis tools

in the field• GPS position

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Intelligent use of data quantity

3. Possibility to create / publish new information

– Scientific algorithms has to be linked to automated data access Provide scientific algorithms as web services

– Users can validate the results of these algorithms based on additional datasets and create new information

• e.g., valid change areas

We need automated data access linked with automated execution of scientific algorithms!

Automated access, analysis, and monitoring of global vegetation time-series data

Automated access, analysis, and monitoring of global vegetation time-series data

Earth Observation Monitor

Datasets: •MODIS 16-Daily Vegetation Index (NDVI, EVI)

Data access:•Pixel or Polygon-based extraction service

Analyses: •Trend calculations•Breakpoint detection•Phenological parameters

Applications:•Web Portal•Mobile App (mobileEOM) for iOS and Android

www.earth-observation-monitor.net

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Earth Change Monitor

• Objectives– Detect areas based on environmental change events in high

density time series data (MODIS)

– Provide high resolution images (Landsat) of the pre- and after change events

– Add further datasets to distinguish between different types of change

– Provide simple tools for users and developers with web services and web interfaces.

Make use of wide variety of available data (data quantity) to create new information to build up a reference database.

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Intelligent use of data quantity

Concept of the „Earth Change Monitor“:

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Focusing on data quality

• We have to consider quality flags (if available!) when using data– Good example: MODIS products

• Crowd-sourced reference database needs to be checked on errors / wrong interpretations– Access only to “experts”

– Users can gain “expert” status for their user account

– Users reference database vs. global reference database

Use data quantity to increase quality of algorithm results (e.g., break-point in vegetation time-series)

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Conclusions

• Crowd-sourced intiative can help scientists to better test their algorithms for information extraction and benefit from the input of users

• Web 2.0 leads us to a new way of – how algorithms can be tested and – how a crowd-sourced reference database can be build up

We need simple web services for data access linked with web services for algorithm execution registered to GEOSS

EO time-series data are better useable and lead to new knowledge to further improve algorithms and validated reference information.

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Thank you for your attention!

Questions?

Contact information:

Jonas EberleFriedrich-Schiller-University

Institute for GeographyDepartment Earth Observation

Loebdergraben 3207743 Jena, Germany

phone: +49 3641 94 88 89email: jonas.eberle@uni-jena.de

Acknowledgement: Friedrich-Schiller-University Jena and EU FP7 EuRuCAS

project (No. 295068) for financing work and travel.