Semi-automated Landscape Analysis Andrew Tewkesbury 3 rd  November 2009
Introduction <ul><li>LandBase </li></ul><ul><ul><li>Development </li></ul></ul><ul><ul><li>Semi-automated processing </li>...
LandBase - Project Brief <ul><li>Review existing land cover schemes and derive an overriding nomenclature </li></ul><ul><l...
Geoperspectives Data Stack
Problems encountered <ul><li>10 TB Data, ~40 TB required to process </li></ul><ul><li>Robust vegetation type differentiati...
Level 2 ‘Core Classes’
Level 1 ‘Environment’
Image Object Hierarchy
Production Process GeoP CIR Lidar DTM & DSM Lidar Buildings Calibration Automated Classification 10km Tiling & Preprocessi...
Ruleset Calibration
Ruleset Calibration
Ruleset Calibration
Ruleset Calibration
Ruleset Calibration
Ruleset Calibration
Automatic Classification Examples
Managing Errors - Smart Editing
Detailed object attribution: 2 levels of classification GeoPerspectives and Lidar height information Local (50m radius) co...
‘ Instant’ Thematic Map Creation – Maidstone, Kent Building Density
Application example – Habitat mapping
Artificial Surface Change 1999 2006 LandBase Artificial Surface Change
DTM Editing – process improvement
Semi-automated reconstruction monitoring
Banda Aceh 2005 - 2007 Classification..
Buildings Change Layer – 2005 to 2007..
Conclusions <ul><li>eCognition facilitates a variety of landscape analyses </li></ul><ul><li>Semi-automated procedures use...
<ul><li>Thank you for your time, </li></ul><ul><li>any questions? </li></ul>
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E Cognition User Summit2009 A Tewkesbury Infoterra Semi Automated Landscape Analysis

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LandBase Semi-automated Landscape Analysis for the United Kingdom

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E Cognition User Summit2009 A Tewkesbury Infoterra Semi Automated Landscape Analysis

  1. 1. Semi-automated Landscape Analysis Andrew Tewkesbury 3 rd November 2009
  2. 2. Introduction <ul><li>LandBase </li></ul><ul><ul><li>Development </li></ul></ul><ul><ul><li>Semi-automated processing </li></ul></ul><ul><ul><li>LandBase application examples </li></ul></ul><ul><li>Other Infoterra Ltd. activities with Definiens </li></ul><ul><ul><li>DTM editing </li></ul></ul><ul><ul><li>Change detection for reconstruction monitoring </li></ul></ul>
  3. 3. LandBase - Project Brief <ul><li>Review existing land cover schemes and derive an overriding nomenclature </li></ul><ul><li>Apply to GeoPerspectives imagery to give a very high resolution classification that can be scaled nationally </li></ul><ul><li>Reviewed, schemes such as LCM2000, NLUD, FAO LCCS, CORINE, GMES CSL and assess customer requirements </li></ul><ul><li>Initial iterative classification testing within Definiens Developer </li></ul>
  4. 4. Geoperspectives Data Stack
  5. 5. Problems encountered <ul><li>10 TB Data, ~40 TB required to process </li></ul><ul><li>Robust vegetation type differentiation extremely difficult from single date AP </li></ul><ul><li>Such a complex data stack strictly limits processing extent and complexity </li></ul><ul><li>Don’t rely on Lidar </li></ul><ul><li>2m DSM, 5m DTM heights a guide only </li></ul><ul><li>Time! </li></ul><ul><li>Change of strategy required………..Generic approach </li></ul>
  6. 6. Level 2 ‘Core Classes’
  7. 7. Level 1 ‘Environment’
  8. 8. Image Object Hierarchy
  9. 9. Production Process GeoP CIR Lidar DTM & DSM Lidar Buildings Calibration Automated Classification 10km Tiling & Preprocessing Smart Editing Product Generation & Export Segmentation GeoP RGB GeoP DTM GeoP Data Stack GeoP DHM Lidar DHM Definiens Enterprise
  10. 10. Ruleset Calibration
  11. 11. Ruleset Calibration
  12. 12. Ruleset Calibration
  13. 13. Ruleset Calibration
  14. 14. Ruleset Calibration
  15. 15. Ruleset Calibration
  16. 16. Automatic Classification Examples
  17. 17. Managing Errors - Smart Editing
  18. 18. Detailed object attribution: 2 levels of classification GeoPerspectives and Lidar height information Local (50m radius) cover percentages Neighbourhood (Zonal) cover percentages
  19. 19. ‘ Instant’ Thematic Map Creation – Maidstone, Kent Building Density
  20. 20. Application example – Habitat mapping
  21. 21. Artificial Surface Change 1999 2006 LandBase Artificial Surface Change
  22. 22. DTM Editing – process improvement
  23. 23. Semi-automated reconstruction monitoring
  24. 24. Banda Aceh 2005 - 2007 Classification..
  25. 25. Buildings Change Layer – 2005 to 2007..
  26. 26. Conclusions <ul><li>eCognition facilitates a variety of landscape analyses </li></ul><ul><li>Semi-automated procedures used to overcome problems associated with: </li></ul><ul><ul><li>Data quality </li></ul></ul><ul><ul><li>Complexity and Knowledgebase gaps </li></ul></ul><ul><ul><li>Transferability </li></ul></ul><ul><li>Balancing ruleset development and manual intervention to achieve operational, cost effective mapping solutions </li></ul>
  27. 27. <ul><li>Thank you for your time, </li></ul><ul><li>any questions? </li></ul>

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