Hannah’s Trustable AI in Mapping (TAiM) project was funded by over £1m from Innovate UK, the UK’s innovation agency. The project has developed new standards to assess the reliability of Artificial Intelligence (AI) algorithms used in environmental mapping.
The consortium of geospatial and environmental data experts issued a joint press release: “From Black Box to Benchmark, Scottish Team Sets New Geospatial AI Standards for the Environmental Sector”.
TAiM established clear evaluation standards for AI-driven environmental mapping, ensuring its credibility and effectiveness in real-world applications.
AI and ML technologies have the potential to revolutionise environmental mapping by automating feature identification and analysis from remotely sensed data. They offer greater efficiency over traditional methods like manual digitisation and Geospatial Information System (GIS)-based operations. However, concerns over accuracy, reliability, and the “black box” nature of AI models have slowed adoption.
To tackle these challenges, the TAiM consortium, led by EOLAS Insight Ltd and Hannah and colleagues at The James Hutton Institute, as well as colleagues at Highlands Rewilding is building a standardised framework to assess AI model performance for land cover mapping. The group has developed example AI algorithms to test the system, generating high-quality validation datasets. The finished frameworks are to be published openly, allowing others to assess and refine their own AI models with greater confidence.
The project concluded in March 2025, having lasted one year. Its key outcome is a website featuring a map-accuracy data portal and a knowledge base.
The data portal enables map producers and users to assess AI-derived maps by comparing them with high-quality field data, including detailed habitat maps, sub-metre forest canopy height and surveys of specific ecosystems like salt marshes, grasslands and peatlands. Meanwhile, the knowledge base offers articles on best practices for understanding and testing AI map accuracy, along with case studies demonstrating how the portal and its data can support effective map evaluation.
Dr Rudman said: “The use of AI for mapping Scotland’s landscape is making the process faster and reducing costs. However, field data will always be needed to drive the modelling, and the data products are interpreted differently from ground survey mapping. The TAiM project is helping to resolve challenges and increase trustability in this new kind of knowledge.”