1. Automating the analysis of remotely-sensed data © Macaulay Land Use Research Institute, L3-Storm, Redleaf Systems, 2000 Landsat TM imagery enhanced by Macaulay

2. 2 ETORA-II A toolkit to facilitate the automatic analysis of remotely-sensed imagery –Faster application development through the use of Commercial Off- the-Shelf (COTS) products; and –Cheaper application deployment through automation An Environment for Task ORientated Analysis

3. 3 Presentation Overview An ETORA-II application The case for automating the analysis of remotely-sensed data Some hard problems, and the limitations of available software What is required for automation, and why L3-Storm and Redleaf? ETORA-II features Summary

4. 4 Developed for the Macaulay Land Use Research Institute, Aberdeen, Scotland Problem: the need to update the Land Cover of Scotland (1988) dataset –Census of Scotland’s land cover (>1300 classes); –20 person years to interpret and digitise aerial photography; –~£2m Solution: SYMOLAC-II, constructed using ETORA-II –Complex rules/regulations, involving multiple datasets requiring a wide range of expertise; –Leading to an automated information system for Scotland’s land cover ETORA-II The LCS8 dataset

5. 5 A collaborative effort –MLURI as an end user; –Redleaf Systems as a software developer; –L3-Storm as a software developer/provider of COTS products; –IPR agreement in place ETORA-II The LCS8 dataset

6. 6 There is an industry-wide need to increase automation The Case for Automation RS data markets RS data volume and variety, availability, awareness An increasing need for human expertise... … and an increasing need for automation Why? –The cost of delivering information to end-users

7. 7 Some Hard Problems There is more to automation than chaining together a series of operations –many different approaches may be possible; –these approaches may not be linear; –the data and knowledge available for one geographical area may not exist for another, or could be of lesser quality; –the results may be conflicting; –non-mathematical knowledge can improve results and increase efficiency; –processing explanations must be easily accessible; and –new data, knowledge and software resources can become available at any time. GIS/GIP packages have not been designed to support such processing

8. 8 Achieving Automation Automation must involve –Knowledge of the problem domain A dedicated reasoning component is required –Command and control of GIS and GIP The COTS approach; Reuse legacy systems A system capable of such automation must be –Flexible to accommodate the varied data, processing, knowledge, and reasoning strategies necessary to solve a problem; –Extensible to allow new data, knowledge, and software resources to be readily and cheaply utilised; and –Adaptable enabling the system to work around typical complexities

9. 9 Achieving Automation What are ETORA-II’s underlying features, those that make it suited to supporting automated applications –Flexible to accommodate the varied data, processing, knowledge, and reasoning strategies necessary to solve a problem; –Extensible to allow new data, knowledge, and software resources to be readily and cheaply utilised; and –Adaptable enabling the system to work around typical complexities

10. 10 ETORA-II Flexibility COTS design –re-use of commercial software Arc/INFO ArcView PV-WAVE COTS Products G2

11. 11 ETORA-II Flexibility: COTS COTS ETORA-II G2 Arc/INFO PV-WAVE Product ArcView Imagine PCI ER-Mapper Legacy systems Further bridges... EDP; RPC; CORBA; COM; Java SYMOLAC-II, … ? Project

12. 12 ETORA-II Flexibility: COTS COTS ETORA-II G2 Arc/INFO PV-WAVE Product ArcView Imagine PCI ER-Mapper Legacy systems Future bridges... EDP; RPC; CORBA; COM; Java SYMOLAC-II, … Project Contributions – Eliminate excessive development; – Reduce risk; and – Minimise programme costs

13. 13 ETORA-II Flexibility COTS design –re-use of commercial software Experts –agent-like collections of knowledge; Experts

14. 14 ETORA-II Flexibility: Experts Collections of application-specific knowledge, represented within the G2 component –Blackboard problem-solving model Experts can utilise G2’s powerful knowledge representation and reasoning capability, and command external software Planning and scheduling experts –Solution methodology is dynamic Concurrent and/or sequential responses Contributions –domain knowledge can be modularised; –many types of representation and reasoning are possible; –iterative, opportunistic reasoning, and “good enough” solutions; –others...

15. 15 ETORA-II Flexibility COTS design –re-use of commercial software Experts –agent-like collections of knowledge Uncertainty handling –hypotheses and evidence Hypotheses and Evidence

16. 16 ETORA-II Flexibility: Uncertainty Derived domain knowledge can be represented as hypotheses, with zero or more evidence items to believe or disbelieve them; Hypotheses and evidence are both created by experts; Uncertainty is a function of the belief; Based on Endorsement Theory (Cohen, 1986) Contributions –uncertainty considered throughout an application; –can support different uncertainty representations.

17. 17 ETORA-II Flexibility COTS design –re-use of commercial software Experts –agent-like collections of knowledge Uncertainty handling –hypotheses and evidence Explanations –HTML statements produced by experts Explanations ?

18. 18 ETORA-II Flexibility: Explanations Experts associate statements with evidence –explanations do not just record expert activity Accessible outwith ETORA-II using any browser Contributions: –solution development; –value-added products

19. 19 Achieving Automation What are ETORA-II’s underlying features, those that make it suited to supporting automated applications –Flexible to accommodate the varied data, processing, knowledge, and reasoning strategies necessary to solve a problem; –Extensible to allow new data, knowledge, and software resources to be readily and cheaply utilised; and –Adaptable enabling the system to work around typical complexities

20. 20 ETORA-II Extensibility New knowledge and data resources must be readily accessible –This property emerges from the use of experts –Adding new data –Adding new knowledge New software resources must be readily accessible –PCI, Imagine, GRASS, ER-Mapper, etc. –G2 supports: data, object, and RPC connectivity over TCP/IP and DECnet; ActiveX, Java, CORBA The COTS advantage

21. 21 Achieving Automation What are ETORA-II’s underlying features, those that make it suited to supporting automated applications –Flexible to accommodate the varied data, processing, knowledge, and reasoning strategies necessary to solve a problem; –Extensible to allow new data, knowledge, and software resources to be readily and cheaply utilised; and –Adaptable enabling the system to work around typical complexities

22. 22 ETORA-II Adaptability Common complexities that require adaptation –uncertainties exist within the reasoning processes; –there is more than one interpretation of an area; –not all areas of interest can be analysed using the most effective data; –not all areas of interest can be analysed using the most effective knowledge The property emerges from the use of experts

23. 23 Achieving Automation This capability is described as task-orientation: the ability to focus on each analysis task, employing the most effective data, method, and software resources to each; The specific features of a system capable of building task- orientated applications are: –The ability to use multi-source data; –The ability to represent and reason with disparate knowledge; –The ability to dynamically adapt analyses to the specific nature of each task, and the “real-world” aspects that might introduce uncertainty; –A bridged COTS environment; and –The ability to generate detailed reasoning explanations.

24. 24 Summary Automation is not a trivial process; GIS/GIP packages do not have the requisite capability; ETORA-II achieves this capability via –A COTS design; –Experts; –Hypotheses and evidence; and –Explanations The toolkit is –Flexible; –Extensible; and –Adaptable This capability is termed task-orientated

25. 25 Commercialisation Our partnership –MLURI as an end user –Redleaf as a software developer –L3-Storm as a software developer, and provider of COTS products We believe that task-orientation is necessary to facilitate greater automation within the analysis of remotely-sensed data; Redleaf and L3-Storm are seeking a complementary partner to further the development of ETORA-II towards a commercially viable product –A commercial data/application provider with interests in global markets