Web services for Improving the development of automatic generalisation solutions

1. Web services for Improving the development of automatic generalisation solutions Nicolas Regnauld Research & Innovarion Ordnance Survey 07th March 2006, Dagstuhl, Germany

2. Outline • Strategy for research in Generalisation at Ordnance Survey • 1:50k generalisation project • Objectives • Algorithms developed • Results obtained • Lessons learned during the project • Lots of past research to build on • Little to reuse • Web services • Conclusions

3. Stage 1: current map series Stage 2: new scales and representation Stage 3: integration of external information ? OS database ? ? External Database ? Research strategy: the aim Derive automatically our current products and new custom products from our single database. 1:10k 1:25k 1:50k

4. Research strategy: the plan We want to build a system that can generate dedicated generalisation applications. Product specs Heart of the system Used for all applications External Database Generic Generalisation System OS Database External information used by the system Dedicated Generalisation Application Manual, then automatic Output of the system Automatic Product

5. Current project: MADGE50kObjectives Prototype to demonstrate the potential of automatic generalisation for producing 1:50k maps from OS data. What do we want out of this project? • An idea of what can be achieved automatically towards the creation of a 1:50k map (Landranger style) from OS base data. • A set of generic algorithms that can be reused for other generalisation application • A knowledge base about how to use the algorithms developed • A set of “processing” specifications, i.e. all the information that the prototype needs to run MADGE50k is only our first step towards a generic generalisation system, so strong emphasis on reusability.

6. Madge50k: algorithms researched • Building generalisation • Rural context • Urban context • Road generalisation • Collapse of dual carriageways • Displacement • Generalisation of the hydrology • Generalisation of woodlands • Generalisation of contours

7. Generalisation of buildings in rural context

8. Identify clusters • Identify the shadow lines of the clusters on the roads • Buffer these shadow lines • Extend the buffers to include the remaining buildings Building generalisation in urban areas

9. Collapse of dual carriageways • Automatic pairing • Skeleton (based on triangulation) • reconnection

10. Hydrology 1- Collapse and classify OS Landranger map (1:50k) 2- Prune and smooth

11. Results Automatic generalisation Manual generalisation

12. What have we learned Building an automatic generalisation prototype is long and costly Reasons: there is little ready to reuse • Lots of valuable research exists, but very few prototypes are available for testing. They proved a concept, and died. => redevelopment required, difficult to know the best approach to follow. • Transfer Research -> Commercial GIS not working: • Limited set of generalisation tools available • Limited control over the tools available often makes them unusable. • Spatial analysis tools mostly absent

13. What does it cost us? • Limited impact on cartographic production lines so far Not many good tools available, on different systems. • New research is getting more and more difficult Difficult to build new research reusing what has already been done, because it is not available, needs to be redeveloped.

14. Generalisation services: benefits & constraints Advantages • Large library of tools available • Provides interoperability between GIS platforms Constraints • Packaging algorithms • Efficiency at runtime Extra time required to translate and transfer data both ways • Maintain servers Mixed Each potential user needs to have a plug-in that will translate data from it’s local data format to the standard format used by generalisation services => The idea is to make the best of the existing prototypes at low cost

15. Generalisation services: potential users • Researchers Can use directly the available services to build new ones Time cost rarely an issue, as long as the transfer and translation times can be evaluated. • Map producers Can use directly to build prototype production systems Processing time is an issue, so once the components of the system are identified, negotiations can take place with the authors to build the production system. • GIS companies Can use the services to evaluate the algorithms, and choose those they want to implement (negotiations with authors) => benefits for everyone, as the GIS version of algorithm would be more stable and efficient and would also be maintained.

16. Conclusion • Putting together an automatic generalisation system today is extremely long and costly because of the lack of available tools. • Generalisation Web Services have the potential to unlock the situation • Once in place, it is easy to use (to publish an algorithm or to use some) • Research is going on at the moment to prove the concept (GiMoDig, University of Zurich, ITC), so this is the right time to plan a long term solution.

17. Thanks for your attention,any questions?