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Reasoning about the Environment

This study delves into the nature of land cover across a geographical area and the varied uses of Land Cover Data. Derived from multiple data sources like aerial photographs and satellite imagery, it discusses the potential for automation in processing this data. It evaluates the limitations and potentials of Artificial Intelligence (AI) technologies, emphasizing the need for greater software flexibility. The prototype AI toolkit, ETORA, offers a dynamic solution strategy for land cover data revision problems, as showcased through the SYMOLAC case. Conclusion highlights the importance of automation in land use research and the promising future of AI technology in this field.

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Reasoning about the Environment

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  1. Reasoning about the Environment ChrisSkelsey Keith Matthews Macaulay Land Use Research Institute

  2. 0 1 km Arable Heather moor Coniferous- plantation Land Cover Data • Describes nature of land cover over some geographical area • Varied uses

  3. Land Cover Data • Derived from an interpretation of varied data and information sources • Aerial photographs • Soil maps • Knowledge of local management • Knowledge of seasonal cycles • Satellite imagery • Can automation play a more significant role?

  4. Established Software • GIS and remote sensing packages • Arc/Info • Smallworld • ER-Mapper • PCI • Erdas Imagine

  5. Problem Complexity • Procedural, quantitative functionality 1988 Areas of forest-felling 1995

  6. Artificial Intelligence (AI) • Production rules • Frame systems • Semantic networks • Neural networks • Fuzzy logic • Dempster-Shafer theories

  7. Limitations of AI Approaches • Data-specific and method-specific • Single software environment • Real-world domain complexities prevent these applications “scaling-up” • Most remain within the research community • Still need greater software flexibility

  8. A Prototype AI Toolkit • ETORA • Developed within G2 • Blackboard reasoning • Re-use of established software • ARC/INFO and PV-WAVE servers • Implementation of endorsement theory

  9. Disparate multi-source data Quantitative and qualitative knowledge Dynamic solution strategies Use of 3rd-party, established software Full reasoning explanations, associated with end-products A Prototype AI Toolkit

  10. 0 1 km A Map Revision Problem • Land Cover of Scotland (1988) dataset • Requires revision Arable Heather moor Coniferous- plantation

  11. “difficult access; >20m from forest boundary” “large enough to be completed felling” “may be a track; one exists within 20m” A Map Revision Problem • SYMOLAC: solves a simple problem, but demonstrates the flexibility of ETORA • Produces a useful product despite real-world complexities

  12. In Summary • Automation is becoming increasingly important • Recognised need for AI technology • AI approaches are often problem-specific, or adopt unsuitable software platforms

  13. Some Conclusions • Prototype ETORA toolkit offers flexibility to solution designer • Exists potential to automate a greater number of processes involved in land cover data production

  14. LADSS • Land Allocation Decision Support System • Evaluates economic impacts of land use strategies • Use of genetic algorithms • Bridge to the Smallworld GIS

  15. Why use G2? • Flexible knowledge-representation • Object-orientated concepts • Ability to visualise the reasoning processes • G2-Gateway

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