Abstract
In a time of extensive global biodiversity loss, conservation efforts increasingly rely on high‐resolution, accurate, up‐to‐date habitat maps to guide decision‐making and strategic interventions. Many products have been developed in response, using machine learning (ML) methods to automate habitat classification, often relying on satellite imagery due to the limited availability of ecological survey data at a landscape scale. To ensure that these products are informed by field‐based evidence, citizen science offers an opportunity to complement traditional, resource‐intensive ecological surveys by enabling data collection at scale We explore the effectiveness of using structured citizen science surveys to train an ML model to predict the extent of lowland heathland in Surrey, UK—one of the most threatened and fragmented habitats in Europe. The model uses 246 citizen science plant surveys (2022–2025) along with 22 remotely sensed predictor variables (vegetation indices, topography, soil type) and was guided by ecological expertise from collaborating practitioners. We produced a high‐resolution (3 m) prediction of lowland heathland extent in Surrey with an overall reported accuracy of 93.4% (based on an 80%/20% split of the data) and 66.6% (based on an assessment of ground‐truth points). This is one of the first projects to employ citizen science‐derived data in combination with ML to make habitat predictions at high resolution. We address challenges in integrating citizen science data with algorithmic thinking, such as loss of nuance when adapting site‐level metrics to manageable scales, geographic uncertainty, and the trustworthiness of species identification. We also explore the variability in the reporting of the accuracy assessment between the ML models and propose a transparent and accessible metric that enables direct comparisons. This project highlights the need for transparent and interpretable models to complement, not replace, the human expertise necessary for the assessment at the ground level. We have produced a replicable and scalable framework that demonstrates the value of citizen science as a powerful, cost‐effective foundation for large‐scale ecological modelling. This approach advances inclusive, scalable methods for biodiversity monitoring and aligns with emerging policy frameworks that emphasise community engagement and digital innovation in nature recovery. Read the free Plain Language Summary for this article on the Journal blog. Read the free Plain Language Summary for this article on the Journal blog.