Mapping Farmed Landscapes from Remote Sensing

TL;DR

This paper presents Farmscapes, a high-resolution, large-scale map of England's rural landscapes created using deep learning, enabling detailed ecological analysis and supporting conservation efforts.

Contribution

Introduction of Farmscapes, the first high-resolution, large-scale ecological map of England generated with a novel deep learning segmentation approach.

Findings

High accuracy in identifying woodland (96%) and farmed land (95%)

Strong segmentation of linear features like hedgerows (F1-score 72%)

Open access via Google Earth Engine facilitates ecological planning

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.