Mapping Farmed Landscapes from Remote Sensing

• URL: http://arxiv.org/abs/2506.13993v1
• Date: Mon, 16 Jun 2025 20:50:05 GMT
• Title: Mapping Farmed Landscapes from Remote Sensing
• Authors: Michelangelo Conserva, Alex Wilson, Charlotte Stanton, Vishal Batchu, Varun Gulshan,
• Abstract summary: We introduce Farmscapes, the first large-scale (covering most of England), high-resolution (25cm) map of rural landscape features.<n>This map was generated using a deep learning segmentation model trained on a novel, dataset of 942 manually annotated tiles.<n>By releasing the England-wide map on Google Earth Engine, we provide a powerful, open-access tool for ecologists and policymakers.
• Score: 1.9049093778958732
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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