Land Use Detection

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  • ----- Meeting Notes (12/5/12 09:57) -----
    I generate the land use classification by using LIDAR and orthoimagery respectively.

  • ----- Meeting Notes (12/5/12 10:07) -----
    My goal is to use current data and method that we learn in class to find a better way to do the land use detection

  • ----- Meeting Notes (12/5/12 10:25) -----
    This is where my AOI is in Google map
    Here are two data that I use
  • Further filter ground.las
    I can get better result by using groundonly las instead of raw las file to extract bare earth
  • Six classes set
  • Worthy to try
  • Land Use Detection

    1. 1. Zhaoying Wei GEOG 8350 Comparison of Land use detection in Niagara Falls using LIDAR and orthoimagery
    2. 2. Outlines  Goal  Area of Interest  Data  Method and Result  Conclusion and Future Work
    3. 3. Land use detection orthophoto LIDAR Goal
    4. 4. AOI
    5. 5. Data Data • point cloud Lidar data • orthoimagery Tools •LAStools •Lidar Analyst •ArcGIS 10.0 • ArcGIS 10.0 • Erdas • ENVI
    6. 6. Method (LIDAR)  Extract Bare earth • strip the existing classes from the LAS file and set the coordinate system and projection (noclass.las) • classify the ground points (ground.las) • create the only ground points (groundonly.las) • generate DEM of groundonly .las • extract bare earth DEM from groundonly DEM
    7. 7. Method (LIDAR) From From raw LAS  Extract Bare earth
    8. 8. Method (LIDAR) Extract buildings footprints • compute the height above the ground • Classify trees and Buidlings (treeNbuilding.las) • generate new bare earth for building and trees extraction
    9. 9. Extract building footprints • point cloud building extraction Method (LIDAR)
    10. 10. Extract building footprints • edit building Method (LIDAR) Before After Remove building Add to building Merge building
    11. 11. Before After Extract building footprints Method (LIDAR) Create new building and close holes Final footprints
    12. 12. Extract tree • point cloud tree extraction • visually edit forests and trees Method (LIDAR)
    13. 13. Method (Orthophoto) Supervised classification • Create the signature file and AOI for each class • Combine the signature file of all classes
    14. 14. Method (Orthophoto) Reclassify • manually digitize AOI • assign new class value to water
    15. 15. Conclusion  LIDAR: • vector results consistent with real terrains • limited classes Orthoimage • numerous classes • low quality and accuracy, water
    16. 16. Future work Measure accuracy of the classification result  improve the quality of signature  better water detection  object-based classification Fusion of LIDAR and aerial image

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