Teaching AI the shape of the countryside
To bridge the gap between pixels and planning, we developed a high-resolution deep-learning framework designed to explicitly map features across the complex patchwork of agricultural land.
Training an AI to recognize specific features of the British countryside like a managed hedgerow requires deep expertise, but we only had a relatively small set of annotated data (~247 km²). To overcome this, we used Remote Sensing Foundations’ (RSF) Vision-Transformer (ViT) Backbone pre-trained on more than 300 million global satellite images. RSF is part of Google Earth AI, our collection of geospatial models and datasets to transform planetary data into actionable insights. By starting with this robust foundation of spatial textures, we fine-tuned the model to recognize the specific nuances of the British landscape with much higher precision.
With this trained model as our foundation, we designed a pipeline to resolve our core spatial, semantic, and scaling challenges.
To handle the layered topology of the countryside, where a stone wall might sit directly beneath the canopy of a hedgerow, we developed a dual-layer labeling system using submeter imagery and 1-meter LiDAR data. This allowed our model to see two things in the same space: (1) the ground-level boundaries (like farmed land or water) and (2) the above-ground features (like the trees and walls that sit on top of them). To fix the artificial slices at tile borders, we developed a scalable algorithm that merges geometries across cells, ensuring every feature is geometrically complete.
We then addressed the semantic challenge. An AI model can easily detect greenery, but it doesn’t naturally know the difference between a small cluster of trees and a long, thin hedgerow. To turn the model’s raw digital outlines into a useful ecological inventory, we applied a mathematical test called the Polsby–Popper compactness score. By analyzing the physical footprint of each detection, we programmatically categorized the countryside’s geometry. We defined woodlands as substantial, contiguous canopies with at least a 30-meter diameter, woody patches as small copses or individual trees, and linear woody features — such as hedgerows and elongated corridors — by their stretched footprints, strictly defined by a compactness score of less than 0.5. This geometric intelligence allowed us to programmatically isolate the long, thin corridors that are so vital for wildlife movement.
