Forests are more than just clusters of trees; they are critical systems that sequester carbon, filter water, and support the biodiversity on which humanity depends. As the world strives to mitigate the climate crisis and halt biodiversity loss, increasing forest habitat is a global priority.

The difficulty lies in land use. With a growing population, the demand for food is increasing, and expanding large-scale forests inevitably competes with the agricultural land needed to meet that demand. This tension creates a key challenge: how do we address climate change and halt biodiversity loss without compromising food security or causing "leakage", where conservation in one area inadvertently shifts environmental degradation to another?

Fine-scale woody features, such as hedgerows and shelterbelts woven among our farms, offer a potential solution. They can enhance carbon storage and biodiversity without displacing crops, yet they are often “invisible” to national forest inventories because they are too small for standard satellite detection.

To make these hidden assets visible, we previously released Farmscapes 2020: the first large-scale, high-resolution map to identify overlooked features like hedgerows and linear woodlands across England, in collaboration with the Leverhulme Centre for Nature Recovery at the University of Oxford. While the initial raster (pixel-based) format was a step forward in detection, real-world applications for landscape restoration and carbon accounting require more than pixels. Today, we’re releasing a vectorized dataset that transforms these maps into an actionable inventory of hedgerows, stone walls, and copses. This new resource empowers landowners and conservationists to measure and expand these fine-scale features throughout the UK.

Moving from a high-resolution raster map to an actionable vector dataset required overcoming technical hurdles at the intersection of spatial topology, semantics, and computational scale.

First, agricultural landscapes present complex spatial topologies. Features are rarely isolated; for example, a hedgerow might border a field or run directly alongside a stone wall, meaning standard single-layer models struggle to represent these overlapping elements. Additionally, processing such a large map requires breaking it into S2-cell tiles (a grid system that flattens our round planet into flat squares on a map), which often results in features being artificially sliced at the tile borders.

Second, there is the question of semantic value. A simple "woody" pixel doesn't distinguish between a forest core, a connective corridor, or an isolated copse. To make the vectorized dataset useful for conservation, we had to find a way to programmatically classify these shapes based on their actual ecological function.

Finally, we faced the problem of computational scale. The sheer size of the high-resolution dataset made standard raster-to-vector operations computationally prohibitive. Processing millions of individual woody features across the entirety of England (an area of over 130,000 km²) required careful data handling to avoid overwhelming traditional systems.

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.

Finally, to address the computational bottleneck and scale this analysis nationwide, we leveraged Google Earth Engine. By processing thousands of independent S2 cells in parallel, we bypassed traditional computational limits, allowing us to generate vector geometries for millions of individual features simultaneously. Together, these advancements allow us to turn a raw map into a functional tool for nature recovery.

While the release of the vectorized dataset is an important step forward, we are already working to further refine the data.

We’re investigating the broader utility of high-precision detection for diverse nature-based solutions, such as supporting the quantification of fine-scale woody features in silvopasture and agrisilviculture systems. This technology could also help identify “leakage” events, ensuring that local gains in carbon and biodiversity are not offset by losses just beyond a project’s boundary. These approaches offer a critical pathway to scale restoration across working lands and address the climate and biodiversity crises without compromising global food security.

By making this data open and accessible, we hope to empower farmers, scientists, and policymakers to protect the small-scale features that make a large-scale difference for our planet.

Learn more about our AI and sustainability efforts by checking out Google Earth AI, Google Earth Engine, and AlphaEarth Foundations.