Nature in focus: AI-powered habitat mapping for a wilder future

The Knowledge Transfer Partnership (KTP) aimed to enhance Derbyshire Wildlife Trust’s capability in delivering high-quality habitat data and supporting Biodiversity Net Gain (BNG) through the integration of advanced remote sensing, data analysis, and automation tools.

With a strategic focus on innovation, the project introduced workflows that were previously underutilised across the charity, both locally and nationally.

Through the collaboration with the University of Derby, the project also supported knowledge transfer and research opportunities, while developing scalable methodologies to automate habitat mapping, enhance landscape monitoring, and embed cutting-edge geospatial techniques into DWT’s conservation work. 

Objectives

• Develop an autonomous tool capable of accurately and rapidly classifying habitats in line with UKHab Level-3 standards.
• Ensure high temporal resolution, cost-effectiveness, ease of use, and classification accuracy using machine learning and satellite data.
• Generate data outputs such as land value, classification confidence, and environmental descriptors to enhance decision-making.
• Integrate emerging tools, including UAV technologies and bioacoustic monitoring, into DWT operations.
• Embed advanced remote sensing workflows into DWT practices to support monitoring, grant funding, and stakeholder reporting.

Impact delivered

While the primary project output - the Supervised Classification Mapper (SCM) - remains under development and in the integration phase, the KTP has already delivered wide-reaching impact across Derbyshire Wildlife Trust’s operations, including:

• Tool Development: SCM is actively undergoing real-world testing to ensure it meets user needs and DWT’s conservation goals.

Operational support: Max has contributed to a variety of high-impact internal projects:

• Developed a crop inundation assessment workflow to support agricultural productivity and land use decisions.
• Standardised and centralised DWT’s GIS datasets, improving accessibility and decision-making.
• Performed historical satellite analysis of bracken encroachment to inform land management strategies.
• Applied machine learning techniques to estimate peat depth and quality, guiding targeted restoration work.
• Created workflows for bioacoustic monitoring to support biodiversity tracking on key reserves.
• Pioneered UAV integration, from regulatory understanding to custom hardware design, enabling in-house drone-based surveying.
• Capacity building: Shared remote sensing, coding, and data analysis expertise across the Trust, increasing internal capability. 

"This KTP has been a unique opportunity to apply advanced remote sensing and geospatial science to real-world conservation. From automating habitat mapping to piloting acoustic biodiversity monitoring, I've been able to deliver meaningful change across projects while developing tools that could have long-term national impact."

-Max Papadopulos, KTP Associate.

"This partnership has demonstrated the immense potential of applied research in remote sensing and machine learning to shape the future of conservation. Max's work bridges scientific rigour with practical impact, creating outputs that benefit both the University’s research agenda and the Trust’s environmental mission."

-Dr Mark Bulling, Academic Supervisor.