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.

An autonomous robot with a large gripper can transform a pile of boulders into huge stone walls without mortar – learning on its own how to place each irregularly-shaped stone as the next building block. The robotic excavator has built a stone wall 6 metres high and 65 metres long through a public park on the outskirts of Zurich, Switzerland. It also used a large shovel to autonomously landscape the park's terrain into terraces. "This is the first work to apply such a robotic excavator for the large-scale construction of permanent dry stone walls," says Ryan Luke Johns at ETH Zürich in Switzerland. Johns and his colleagues equipped the robot with lidar, which employs lasers to measure distances, so it could create its own 3D map of a construction site. They also trained several artificial intelligence models to help the robot figure out the best way to grasp and place individual stones.