Boosting biodiversity in transport projects is a must in today’s industry, with pressures from all angles to prove their sustainability and respect for the environment. Beyond the ethical responsibilities to protect and enhance biodiversity, there are also regulatory pressures not to damage nature, pollute the environment or overbuild.

Legislation such as the Wildlife and Countryside Act 1981 seeks to ensure that species are protected, while a series of further rules and regulations related to biodiversity mean that transport professionals not only need to protect nature in and around a site area while a project is carried out, but increase biodiversity compared to pre-development. 

Net gain target

The importance of biodiversity must be considered before a project has begun. Biodiversity net gain (BNG) is a legal requirement for any project that goes through planning, with a 10% improvement on the baseline figure as a minimum target. To help achieve BNG in the most efficient way, AI can have a key role and one of the key tools for many is AiDash. It’s a system that Amy Redpath, senior environmental advisor at M Group, has extensive experience of.

“Following a successful pilot in Peterborough, we have rolled out AiDash across our operations to improve how BNG assessments are completed, with a focus on improved efficiencies,” she explains. “AiDash uses satellite imagery and AI to map habitats more quickly than traditional field surveys. Trial results on a carriageway improvement project in Peterborough showed habitat mapping could be completed 33% faster, with the potential to reduce overall time and costs by up to 50%.”

Redpath says that M Group has also been using the software to map large areas, such as road corridors across a county to identify biodiversity risks and enhancement opportunities. “We use the maps and data and overlay this with a range of data from sensitive receptors, areas of designation and key environmental features to determine where habitats can be improved to increase biodiversity and ecosystem services,” she says.

An evolving landscape

While some may doubt the accuracy of an AI-based model compared to human experience, Redpath confirms that AiDash is very close to reality. “We’ve done quite a few new projects with local authority clients who want to improve biodiversity in 30% of their land by 2030,” she says. “That means thousands of hectares of land which we can assess using AiDash. Through verification with our ecologists, the data is very accurate. We can also overlay the output with data such as flood risk and air quality levels, communities and habitats, and other potential risks. If an ecologist went out and gathered this data it would take years.”

“Since AI is evolving all the time, the opportunities for it to help biodiversity levels are growing. “We’re using AiDash on a large scale now, but ideally it would be much bigger so that it could be used to map at a county-wide level,” says Redpath. “It would be amazing if we could start combining data sources across major projects to get a better idea of what’s happening across a wider area then make improvement plans on top of that.

Predictive modelling of future climate patterns, such as rainfall, would be useful to add to the model,” she continues. “That would help us take a wider landscape-scale approach to design to understand how we could link habitats to improve connectivity and make the landscape more resilient, particularly over the 30-year BNG period.” 

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