Mapping Farmed Landscapes from Remote Sensing
This provides a tool for ecologists and policymakers to enable data-driven habitat restoration and monitoring, addressing a domain-specific need in agricultural management.
The paper tackled the problem of lacking detailed ecological maps for agricultural landscapes by creating Farmscapes, a large-scale, high-resolution map of rural features in England, achieving high f1-scores such as 96% for woodland and 72% for hedgerows.
Effective management of agricultural landscapes is critical for meeting global biodiversity targets, but efforts are hampered by the absence of detailed, large-scale ecological maps. To address this, we introduce Farmscapes, the first large-scale (covering most of England), high-resolution (25cm) map of rural landscape features, including ecologically vital elements like hedgerows, woodlands, and stone walls. This map was generated using a deep learning segmentation model trained on a novel, dataset of 942 manually annotated tiles derived from aerial imagery. Our model accurately identifies key habitats, achieving high f1-scores for woodland (96\%) and farmed land (95\%), and demonstrates strong capability in segmenting linear features, with an F1-score of 72\% for hedgerows. By releasing the England-wide map on Google Earth Engine, we provide a powerful, open-access tool for ecologists and policymakers. This work enables data-driven planning for habitat restoration, supports the monitoring of initiatives like the EU Biodiversity Strategy, and lays the foundation for advanced analysis of landscape connectivity.