Building A Global Road Database? Possibilities and Techniques for Mapping Rural Roads

1. Building A Global Road Database? Possibilities and Techniques for Mapping Rural Roads Chris Funk NCRST

2. Rondonia – Matched Filter NCRST

3. Overview • Motivation • Q: Why build a Global Database of Roads? • A: There is only one world • ‘Nature’ <> ‘Society’ • (ecologos) <> (economos) • Developed <> undeveloped • Roads link societies to nature • communities to the global economy • What defines utility • Consistent, Accurate, Available, Repeatable (CAAR) • Examples of Global Databases: DCW, ETOPO30 • Global Road Database • sources of information • Algorithms • Matched Filter • Multi-spectral Analysis • Texture Analysis NCRST

4. Human Impacts: Fire in Africa Roads increase probability of burns NCRST

5. Human Impacts: DMSP Fires in Indonesia Fires influenced by ENSO and Global Warming Climate influenced by CO2 emissions NCRST

6. Roads and Deforestation in the Amazon Rondonia 1975 Rondonia 1992 Source – USGS Earthshots NCRST

7. GRD Application – Disaster Mitigation • Case Study: Flooding in Mozambique • Context: in Winter of 2000 tropical cyclones brought massive flooding to Southern Africa • Largest single threat was lack of access to good drinking water • Improved knowledge of roads would have aided relief efforts Images www.disasterrelief .org Taken at Relief Station at Gode NCRST

8. UncertainHuman Futures Increasing populations strain food production Increasing temperature strain tropical climates NCRST

9. Solution? Data RS • Improve current knowledge by harnessing the power of geographic science • Improved knowledge increases the quality of response Knowledge GIS Wisdom .txt Action Policy NCRST

10. Utility Definition • Consistency • Accuracy • Availability • Repeatability NCRST

11. Geographic Science Provides Utility Spectral Libraries and Spectral Analysis methods are tied to invariant physical properties of stuff • Remote Sensing techniques can be applied uniformly across space • Remote Sensing techniques can be applied uniformly across time NCRST

12. Example of a High Utility ‘Physical’ Dataset • USGS ETOPO30 • 30 m Digital Elevations • Global Coverage • Universally Available • Many derived products • Surface topology • Stream networks NCRST

13. Example of a High Utility DatasetDigital Chart of the World • 1:1,000,000 • global data • Created by ESRI • Repeatable? NCRST

14. GRD Potential Data Sources 100 101 102 103 IKONOS AVIRIS TM Inexpensive Widely Available Spatial [m2] AVHRR 100 101 102 103 Spectral Bands NCRST

15. GRD – Potential Algorithms • Matched Filtering • Sub-pixel detection strategy • Applicable where spectral signal is distinct, but weak • Spectral Mixture Analysis • Breaks pixel into sub-components • Useful when road has strong soil component • Roads can also appear as high error pixels • Texture Analysis • Use spatial information to isolate road pixels • Applicable in situations where no systematic difference in road material exists NCRST

16. Matched Filtering-I Rotate Data Cloud To Maximize Signal NCRST

17. Clustered Matched Filtering-II NCRST

18. MF exampleTM Rondonia 1998 – Bands 345 NCRST

19. Rondonia Example – Bands 123 NCRST

20. Rondonia – Matched Filter NCRST

21. Rondonia – Hi Pass – Band 1 NCRST

22. Rondonia – 1998 – Local Range NCRST

23. Rondonia1996 SMA Error NCRST

24. Summary • Extraction of Rural Roads from TM imagery seems practical and plausible • Library-based spectral techniques perform well • We can and should build a global road database: • Based on TM imagery • 100% coverage • ‘easily’ updatable • freely available • Future directions • Improved spectral libraries • Santa Barbara Testbed – algorithm evaluation • Application/testing of rural road extraction techniques in US and Brazil NCRST