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Terraviso
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TerraViso Solution
  • TerraViso Introduction
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  • BNG Module
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White Papers
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  • BNG vs Nature Risk
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  • Home
  • TerraViso Solution
    • TerraViso Introduction
    • Stack Builder
    • Analysis
    • Viewer
    • Basic Biodiversity Module
    • BNG Module
    • Portfolio Management
  • Frameworks
    • Frameworks/Outcomes
  • Industries
    • Industries
    • Banking
    • Developers
    • Ecology Consultants
    • Insurance
    • Land Owners and Managers
    • Planning
  • White Papers
  • Intelligence
    • TVBI
    • Ecological Fingerprinting
    • Scenario Intelligence
    • BNG vs Nature Risk

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A vibrant yellow flower field under a clear blue sky with a green forest backdrop.

TerraViso Analysis

Understand your results

TerraViso Analysis allows users to turn prepared data into insight. This is where stacked imagery and spatial layers are analysed to classify habitats or land cover, calculate areas and metrics, and compare conditions across time. Users can run classifications, explore baseline versus future or post-development scenarios, and generate summaries that quantify change in a clear, spatially explicit way. The Analysis page bridges technical processing and real-world decisions, producing maps and evidence that can be reviewed, explained, and reused across planning, biodiversity, and risk workflows. 

 The Analysis tab gives you insight into how your model performs. 

  • Train habitat classification models – use your data to build robust, site-specific models 
  • Apply models to new areas – generate habitat maps quickly and consistently 
  • Review model performance – understand accuracy, confusion, and class-level results 
  • Refine training data interactively – improve outputs with iterative learning 
  • Generate probability and confidence layers – see where predictions are strong or uncertain 
  • Maintain consistency across projects – apply the same methodology at scale 

Why it matters

  • Move from manual mapping to automation – drastically reduce time spent digitising habitats 
  • Improve accuracy and consistency – minimise subjectivity in habitat classification 
  • Build defensible models – support decisions with transparent, repeatable analysis 
  • Adapt to different landscapes – tailor models to local conditions and imagery 
  • Reduce uncertainty in outputs – understand where results are reliable and where to review

What it enables

  • Rapid habitat mapping at scale – analyse large areas in a fraction of the time 
  • Continuous model improvement – get better results with every iteration 
  • Integration with BNG workflows – feed directly into baseline and post-development assessments 
  • Data-driven decision making – replace guesswork with measurable, evidence-based insights 
  • A foundation for advanced analytics – enable change detection, monitoring, and future scenario modelling

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