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E Cognition User Summit2009 Jo Neil Dunne Uvm Mapping Green Infrastructure
 

E Cognition User Summit2009 Jo Neil Dunne Uvm Mapping Green Infrastructure

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

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

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E Cognition User Summit2009 Jo Neil Dunne Uvm Mapping Green Infrastructure E Cognition User Summit2009 Jo Neil Dunne Uvm Mapping Green Infrastructure Presentation Transcript

  • Mapping the Green Infrastructure
    Jarlath O’Neil-Dunne
    University of Vermont
    Spatial Analysis Laboratory
    E-mail: joneildu@uvm.edu
    Blog: Letters from the SAL
  • 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    Everything in my presentation is obsolete thanks to eCognition 8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
  • We Have Problems
    Source: Chesapeake Bay Program
  • There Are Solutions
    Source: Chesapeake Bay Program
  • Actionable Intelligence
    Mayor Ellen Moyer agrees to a 50% Urban Tree Canopy in Annapolis by 2036
  • Needs Not Met by Existing Data
  • Data ≠ Information
  • Data + OBIA = Information
  • 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
  • OBIA Systems
    Requirements
    Components
  • Jefferson County
  • Multi-Sensor Data Fusion
  • Overlapping Tiles
    10%
    10%
  • Customized Import
  • Batch Processing
  • Replicating the Human
  • The Default Approach
    Segmentation
    Segmentation
    Objects
    Objects
    Classification
    Classification
  • eCognundrum
  • Fine Scale Segmentation
  • Coarse Scale Segmentation
  • Cognitive Approach
    Segmentation
    Fusion
    Morphology
    Classification
    Objects
  • Iterative Approach
  • Class Hierarchy
  • Rule Set
  • Incorporating Vector GIS Data
    Original buildings
    Shrink based on nDSM
    Find tall
    Grow into tall
  • Image Processing
  • Image Processing
  • Stepwise Approach
    Segmentation / classification
    Reclassify based on size
    Refinement on large
    Result
  • Morphology (Tree Canopy)
    Original objects
    Active & target classes
    Focus Area
    Grow out
    Grow in
    Reshaped objects
  • Morphology (Buildings)
    Original objects
    Target, active, & focus area
    Grow & shrink
    Final objects
  • Incorporating Vector GIS Data
    Chessboard segmentation
    Focus area
    Grow loop
    Final objects
  • Object Hierarchy
    Classification to this point
    Object hierarchy
    Urban area
    Impervious within urban
  • Multi-Sensor Data Fusion
  • Contextual Operations
    Original classification
    Growing
    Edge detection
    Competitive growing
  • Object Fusion
  • Consistent Output
  • Going Beyond Human Cognition
    • S
    • P
    • A
    • N
    • I
    • E
    • L
    patial
    rocessing
    nalysis
    onhuman
    ntelligent
    xperts
    abratories
  • 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