From pixels to planning: Earth AI for nature restoration

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From pixels to planning: Earth AI for nature restoration

June 16, 2026<br>Michelangelo Conserva, Research Scientist, and Charlotte Stanton, Senior Program Manager, Google Research

We developed a high-resolution deep learning framework to reveal fine-scale ecological features, like hedgerows and copses, that are typically invisible to standard satellite detection. This precise vector data offers a new pathway to address the climate and biodiversity crises on working lands without compromising food security.

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Vectorized Farmscapes 2020

Farmscapes 2020

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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.<br>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?<br>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.<br>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.

Key landscape features and their primary ecological functions.

Mapping the fabric of the countryside

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.<br>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.<br>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.<br>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.

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.<br>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.<br>With this trained model as our foundation, we designed a pipeline to resolve our core spatial, semantic, and scaling challenges.<br>To handle the layered...