document title / titre du document TRP W ORK PLAN ... - emits - ESA

TRP Work Plan 2005-2007
Description of Activities
TEC-SB/7935/dc
12/Feb/09
1.4.4 - Payload Data Exploitation (Source: TRP 2000-2003)
TRP Reference: PDE-KEO-001
Title: Knowledge Centred Earth Observation - Phase 1
Deliverables:
Current TRL: Target TRL: Application Need/Date:
Application/Mission:
Contract Duration:
SW Clause : - Dossier0 Ref.: T-572
Consistency with Harmonisation
Roadmap and Conclusions:
TRP Reference:
Title: Knowledge Centred Earth Observation - Phase 2
Today, a big discrepancy exists between the current EO data offers (data sets or images) and the real user need
(information). The manual process mainly performed by experts to mine information from images is currently too complex and
expensive to be applied systematically or even on just an adequate number of acquired scenes. This hinders the access to
the petabytes of available or new data, penalises large projects of human relevancy like those related to environment
monitoring, and might even leave totally undetected important events.
The situation might become worse in future, since, in order to cope with requirements for monitoring events as well as global
and local changes, more missions and also constellations are being deployed, with broader sensor variety (e.g.: gas detection,
hyper-spectral), increasing data rates (higher resolution, number of channels) and increasing complexity (e.g. data formats).
Therefore, the user desire for value-added information (focused, concise, reliable, timely, understandable, compliant with user
processes) will likely remain unsatisfied, despite the availability of potential answers in the EO data, unless a breakthrough is
introduced. Emerging technologies, which could support / ease experts’ tasks also by applying their knowledge to large data
sets, could provide this breakthrough. These technologies should perform automatic information mining and classification,
through the support of “intelligent” learning systems capable to discover, store and use the knowledge necessary to retrieve
and fuse data and information from EO and non-EO fields, to model user semantic and domain of interest, and to mask the
inherent complexity of the entire mechanism.
The aim of the KEO research activities is to foster the enlargement of EO data utilisation, and in particular of the large
archives of multi-mission and multi-temporal images. This should be obtained through a better support to research,
value-adding industry, service providers and EO user communities (like those involved in scientific investigations, in risk and
disaster management, or in the Global Monitoring for Environment and Security programme), by permitting in sequential
steps:
• Identification of the relevant data by its content (and not simply on the basis of spatio-temporal queries)
• Direct access to the information contained in the data (as opposite to access to the images).
This aim can be pursued identifying technological opportunities for the following main objectives:
• Information mining and classification based on algorithms or knowledge for bulk EO data processing at large archive centres
or at small companies (which manage limited but specialised data sets); in particular data relevant to classification attributes
(also for enhanced catalogue searches) and / or information (avoiding the need to access the images), through:
- Interactive Information mining based on acquired knowledge
- Bulk classification based on acquired knowledge
- Bulk classification based on feature extraction algorithms
• Intelligent human interfaces, characterised by:
- Learning capability for knowledge transfer (from experts) or its dynamic acquisition during user sessions
- Capability to understand / talk user semantic
- Capability to support complex multidiscipline queries
- Advanced features (including graphical / multi-modal / multi-media interactions, self-adaptation to user interest,
recommending, etc.)
• Knowledge Discovery / Management
• Advanced Support Techniques (e.g.: for data / information fusion, information management, time series handling, etc.).
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