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E Cognition User Summit2009 A Tewkesbury Infoterra Semi Automated Landscape Analysis
 

E Cognition User Summit2009 A Tewkesbury Infoterra Semi Automated Landscape Analysis

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LandBase Semi-automated Landscape Analysis for the United Kingdom

LandBase Semi-automated Landscape Analysis for the United Kingdom

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    E Cognition User Summit2009 A Tewkesbury Infoterra Semi Automated Landscape Analysis E Cognition User Summit2009 A Tewkesbury Infoterra Semi Automated Landscape Analysis Presentation Transcript

    • Semi-automated Landscape Analysis Andrew Tewkesbury 3 rd November 2009
    • Introduction
      • LandBase
        • Development
        • Semi-automated processing
        • LandBase application examples
      • Other Infoterra Ltd. activities with Definiens
        • DTM editing
        • Change detection for reconstruction monitoring
    • LandBase - Project Brief
      • Review existing land cover schemes and derive an overriding nomenclature
      • Apply to GeoPerspectives imagery to give a very high resolution classification that can be scaled nationally
      • Reviewed, schemes such as LCM2000, NLUD, FAO LCCS, CORINE, GMES CSL and assess customer requirements
      • Initial iterative classification testing within Definiens Developer
    • Geoperspectives Data Stack
    • Problems encountered
      • 10 TB Data, ~40 TB required to process
      • Robust vegetation type differentiation extremely difficult from single date AP
      • Such a complex data stack strictly limits processing extent and complexity
      • Don’t rely on Lidar
      • 2m DSM, 5m DTM heights a guide only
      • Time!
      • Change of strategy required………..Generic approach
    • Level 2 ‘Core Classes’
    • Level 1 ‘Environment’
    • Image Object Hierarchy
    • 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
    • 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) cover percentages Neighbourhood (Zonal) cover percentages
    • ‘ 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
      • eCognition facilitates a variety of landscape analyses
      • Semi-automated procedures used to overcome problems associated with:
        • Data quality
        • Complexity and Knowledgebase gaps
        • Transferability
      • Balancing ruleset development and manual intervention to achieve operational, cost effective mapping solutions
      • Thank you for your time,
      • any questions?