Funded by Innovate UK, the Trustable AI in Mapping (TAiM) initiative is developing clear evaluation standards for AI-driven environmental mapping, aiming to boost credibility and effectiveness in real-world applications.

AI and machine learning technologies offer the potential to transform environmental mapping, enabling the automated identification and analysis of features from remotely sensed data. These technologies promise greater efficiency compared to traditional methods such as manual digitisation and GIS-based operations.

However, concerns about accuracy, reliability, and the opaque “black box” nature of AI models have slowed wider adoption.

“Without standardised assessment methods, users struggle to fully trust AI-driven mapping solutions,” says Douglas McNeil, Managing Director of EOLAS Insight Ltd. “By developing ‘trustable AI,’ we can help users understand the strengths and limitations of these technologies, fostering confidence in their application.”

Led by EOLAS Insight Ltd, the TAiM consortium – which includes Agrimetrics, Highlands Rewilding, the James Hutton Institute, and Omanos Analytics – is tackling these challenges by creating a standardised framework to assess AI model performance for land cover mapping.

This work involves developing example AI algorithms to test the framework, alongside generating high-quality validation datasets. The resulting frameworks will be openly published, helping organisations assess and refine their own AI models with greater confidence.

“Setting industry standards and encouraging open methodologies will accelerate AI adoption in geospatial applications,” McNeil adds. “By building trust through transparency, we can support data-driven decision-making that benefits the entire environmental sector.”

The key outcome of the year-long project, concluding in March 2025, will be a dedicated website featuring a map-accuracy data portal and a knowledge base. The data portal will allow map producers and users to assess AI-derived maps by comparing them against high-quality field data, including detailed habitat maps, sub-metre forest canopy height data, and surveys of specific ecosystems such as salt marshes, grasslands, and peatlands.

Meanwhile, the knowledge base will offer articles on best practices for understanding and testing AI map accuracy, along with case studies showing how the portal and its data can support effective map evaluation.

Several consortium members have highlighted the project’s value:

Pierre Stratonovitch, Data Scientist at Agrimetrics, says: “Agrimetrics developed new Earth Observation land cover mapping models to identify a range of natural habitats, woodlands, and crop species.

“It is always difficult to obtain high-quality validation datasets and so we’re delighted that the TAiM project generated an impressive dataset offering a wide range of observations. The assessment frameworks developed by the consortium will set the industry standard on how Earth Observation models should be assessed.”

Cathy Atkinson, Chief Data Scientist at Highlands Rewilding, says: “Highlands Rewilding has carried out detailed ground surveys across a range of habitats, including woodland, grassland, peatland and saltmarsh. These ecological surveys provide robust data against which outputs from AI algorithms can be assessed.

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“This data also demonstrates how ground surveys can be used together with AI to reduce errors and uncertainties in environmental monitoring, vital for the natural capital industry.”

Dr Hannah Rudman from the James Hutton Institute, who worked on the project with her colleague Dr Matt Aitkenhead, says: “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.”

Clare Rumsey, Technical Director at Omanos Analytics, adds: “At Omanos Analytics, we focused on understanding ground-truthing needs for Earth Observation-based, AI-driven mapping. To measure AI mapping accuracy, we supported fieldwork that surveyed areas using a classification system suitable for satellite-derived imagery.

“We also developed ‘QuickBeam’, a new smartphone app designed for simple, user-friendly ground-truthing of AI mapping products. By allowing users to add extra information like condition and biodiversity, Quickbeam brings together the power of EO and on-the-ground expertise to mapping products, increasing confidence and trust for non-experts.”

The TAiM website and data portal (which was not live at the time of writing) will be found at: https://trustable-mapping.xyz.