Department of Earth Observation Science
Expertise
Within the ITC's core knowledge the EOS department contributes to the research programme in the various components. One activity is to do core science research in data mining, 3D GIS and Earth Observation for various applications. In addition, it provides support to other departments. Definition of the knowledge nodes is governed by the type of geo-information that is to be produced. Knowledge development aims to contribute to all 5 spearheads as defined in the ITC strategic plan.
The knowledge nodes for the EOS Department are:
Spatio-temporal statistics
This node contains the primary ITC knowledge on data quality, data mining and thematic geo-information. Typical expert fields of this knowledge node are image analysis tools (wavelets, MRF, NN), monitoring in relation to models, space-time statistics, image classification and segmentation and image fusion. In principle this node is not restricted to a particular sensor type, although some emphasis is given on radar and in particular InSAR.
Main application fields are natural and water resource management and geo-sciences.
Topographic Mapping
This node contains the primary ITC knowledge on extraction and interpretation of topographic information. Typical expert fields of this knowledge node are geometric aspects of sensor systems, object based image analysis (e.g. modelling of object knowledge, perceptual grouping), data acquisition standards, image sequence processing, digital camera and lidar technology.
Innovative and emerging opportunities
In a range of partner institutes we can identify a need to have a better use of available remote sensing information. This information is available, and still promising in many aspects. It is a challenge to get the most out of collected data, whereas modern methods may be helpful as well to solve existing problems. Increasing spatial resolution, increasing temporal frequency, the advent of modern multi-sensor systems give us a new challenge to apply, adapt and modernize approaches we are taking thus far. Much depends upon the quality of the data and the objects the data supposedly represent.
For all these reasons, the EOS Department in the recent past has focused on innovative and emerging opportunities as identified by BSIK, European Union (FP6), SRON and WOTRO. It aims to continue to do so in the years to come.
Knowledge nodes in relation to focus/themes
Modern photogrammetry and remote sensing is governed by the existence of more and more available sensors, also in developing countries, and by issues of data quality. Processes (in urban studies, in biodiversity, in agriculture) are to be monitored and available tools are to be utilized to the utmost degree. We see it as a major challenge to be leading in the geo-information science and earth observation, to take advantage of the latest technological developments, and to make the most out of these in the frame of relevant questions. Data quality is an increasingly important issue in the space-time domain, as information is readily available, but often difficult to interpret and assess. The two knowledge nodes express these concerns and focal issues. Moreover, spatio-temporal statistics has been proven useful for studies related to (hydrological) models, vegetation (biodiversity) and studies on wildlife and soil and landscape studies.
Complementary research partners in the Netherlands, Europe, emerging economies, developing countries
Complementary research partners in the Netherlands are both the small and medium-sized companies and consultance offices, as well as relevant divisions in larger companies. Criteria that we maintain are a balance in terms of exchange of knowledge, well covered financial aspects and a proper 'feeling' with the mission of the ITC.
Approach to knowledge development with special attention to intra-departmental/ITC coordination/collaboration
The EOS Department has an open door towards discussions with interested and dedicated members of other (applied) departments. A natural collaboration exists with the GIP department, in particular because of the shared responsibilities towards the GFM course and cooperation in technical matters of developing SDIs. Advices in the field of sensor methodology, spatial/temporal statistics and photogrammetry are generously provided. Out of good principle the department has an open door towards any specific well-identified initiative in those fields. Adversely, we have the policy that we try to link up with activities that we recognize and are aware of at the other departments. We will try to supply knowledge, skills and methods wherever we see an opportunity. Wherever appropriate the department seeks data sets and well-defined problems in all of the other departments.
Output targets as part of ITC targets
- The EOS department aims to have 5 PhD students by the beginning of 2006.
- The EOS department aims to continue and expand its in distance education efforts by facilitating 2 new courses in 2005/6 and the aim to offer 3 courses by 2009.
- The EOS department aims to participate in 3 refresher courses every year.
- The EOS department aims to actively participate in GFM2 - 4 education.
- The EOS department aims to further enhance collaboration with partners in India and China, being front running countries in launching new satellites, Namibia, Nigeria and Tanzania where good relations have been built up and with other possibilities like the Middle East and Mongolia. Further possibilities with at least one Latin American country will be explored.
- The EOS department aims to actively seek for collaboration with former Soviet Union states and new members of the EU.
No crisp bounds exist between the fields and there are several expert fields from which both knowledge nodes should benefit. E.g., image classification is put in the Spatio-temporal statistics knowledge node, but can also be used for change detection needed for map revision. Vice versa, lidar technology is put in the Topographic mapping knowledge node, but will also be useful in the application fields occurring in the other knowledge node.