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TerraViso Stack Builder
Build the foundation for accurate results
The TerraViso Stack Builder enables users to assemble the core spatial evidence that underpins all later analysis. The Stack Builder allows you to load and combine multiple imagery and data sources—such as satellite imagery, aerial photography, and derived indices—into a single, aligned multi-band stack. This process standardises resolution, extent, and structure, ensuring that every downstream task (habitat classification, change detection, biodiversity metrics, or risk assessment) is based on a consistent, repeatable input. In practice, it’s where raw data is turned into decision-ready evidence: a clear, auditable foundation that can be reused across projects, time periods, and analytical workflows.
- Combine multiple imagery sources into one dataset – bring together aerial, satellite, and derived layers in a single stack
- Automatically generate key indices – create NDVI, NDWI, and other features that reveal vegetation health and water presence
- Enhance data with texture and context – add spatial patterns and edge information to improve habitat detection
- Standardise inputs for modelling – ensure consistent, aligned data ready for training and classification
- Improve classification accuracy – provide richer inputs that help distinguish habitats more reliably
- Remove manual preprocessing steps – automate complex GIS workflows behind the scenes
Why it matters
- Better data = better results – high-quality inputs directly improve habitat classification and BNG outputs
- Unlock insights not visible to the eye – detect subtle differences between habitats using spectral indices
- Increase model confidence – reduce uncertainty with more robust and consistent data inputs
- Work at scale efficiently – process large areas quickly without manual GIS effort
- Create a defensible workflow – ensure your analysis is repeatable, consistent, and auditable
What it enables
- Automated habitat classification – turn raw imagery into mapped habitats quickly and consistently
- Faster project setup – generate analysis-ready data in minutes instead of manual GIS preparation
- Scalable assessments – run biodiversity analysis across large sites or entire portfolios
- Change detection over time – compare multiple vintages to understand habitat trends and impacts
- Active learning workflows – improve models continuously with targeted training data
- Higher confidence BNG outputs – feed stronger inputs into baseline, post-dev, and scenario analysis
- A fully integrated workflow – connect imagery, modelling, and BNG reporting into one seamless system