This research explores how remote sensing and machine learning can support biodiversity monitoring, particularly to meet the UK’s new Biodiversity Net Gain (BNG) requirements. Traditional habitat mapping relies on time-consuming field surveys, but remote sensing and machine learning may offer scalable and faster assessments of habitats and biodiversity.

My work aims to bridge the gap between ecologists, remote sensing specialists, and machine learning experts by developing workflows and tools that simplify habitat classification and biodiversity monitoring, providing practical guidance for ecological consultancy and conservation efforts.