Mapping the green infrastructure. Single tree inventory using LiDAR and Airborne CIR data.

1. Mapping the Green Infrastructure Jarlath O’Neil-Dunne University of Vermont Spatial Analysis Laboratory E-mail: joneildu@uvm.edu Blog: Letters from the SAL

2. Disclaimers Thank you to the German education system Everything in my presentation is obsolete thanks to eCognition 8 Todd Taylor forgot to order the smoke machine I requested What you will see at the end of this presentation will revolutionize OBIA

3. We Have Problems Source: Chesapeake Bay Program

4. There Are Solutions Source: Chesapeake Bay Program

5. Actionable Intelligence Mayor Ellen Moyer agrees to a 50% Urban Tree Canopy in Annapolis by 2036

6. Needs Not Met by Existing Data

7. Data ≠ Information

8. Data + OBIA = Information

9. The Problem with 80% All that is clear is that the present acceptance of 80% accuracy is resulting in poorer data being delivered to decision makers than was routinely delivered, somewhat more slowly, by human interpreters in the five decades since the end of World War II. C.E. Olson, Jr. Is 80% accuracy good enough? Proceedings from Pecora 17, 2008

10. OBIA Systems Requirements Components

11. Jefferson County

12. Multi-Sensor Data Fusion

13. Overlapping Tiles 10% 10%

14. Customized Import

15. Batch Processing

16. Replicating the Human

17. The Default Approach Segmentation Segmentation Objects Objects Classification Classification

18. eCognundrum

19. Fine Scale Segmentation

20. Coarse Scale Segmentation

21. Cognitive Approach Segmentation Fusion Morphology Classification Objects

22. Iterative Approach

23. Class Hierarchy

24. Rule Set

25. Incorporating Vector GIS Data Original buildings Shrink based on nDSM Find tall Grow into tall

26. Image Processing

27. Image Processing

28. Stepwise Approach Segmentation / classification Reclassify based on size Refinement on large Result

29. Morphology (Tree Canopy) Original objects Active & target classes Focus Area Grow out Grow in Reshaped objects

30. Morphology (Buildings) Original objects Target, active, & focus area Grow & shrink Final objects

31. Incorporating Vector GIS Data Chessboard segmentation Focus area Grow loop Final objects

32. Object Hierarchy Classification to this point Object hierarchy Urban area Impervious within urban

33. Multi-Sensor Data Fusion

34. Contextual Operations Original classification Growing Edge detection Competitive growing

35. Object Fusion

36. Consistent Output

38. P

39. A

40. N

41. I

42. E

43. Lpatial rocessing nalysis onhuman ntelligent xperts abratories

44. Mapping the Green Infrastructure Jarlath O’Neil-Dunne University of Vermont Spatial Analysis Laboratory E-mail: joneildu@uvm.edu Blog: Letters from the SAL Funding from: USDA Forest Service National Science Foundation Jefferson County Commission