From pixels to planning: Earth AI for nature restoration
Google Research developed a high-resolution AI system that converts satellite imagery into vector data identifying small ecological features such as hedgerows, stone walls, and copses. These features can store carbon and support biodiversity without taking agricultural land out of production, but they are often missed by conventional forest inventories. The resulting open dataset aims to make fine-scale nature restoration measurable and actionable across the UK. ## The case for fine-scale restoration - Forests support carbon sequestration, water filtration, and biodiversity, but expanding forests can compete with food production. - Conservation efforts can also create “leakage,” shifting environmental damage elsewhere. - Hedgerows, shelterbelts, and other woody features offer ecological benefits while remaining integrated into farmland. - Google’s earlier **Farmscapes 2020** raster map detected these features across England, but pixel data was insufficient for planning, restoration, and carbon accounting. ## Challenges in mapping the countryside - Agricultural features often overlap spatially, such as hedgerows growing alongside or over stone walls. - Processing data in S2-cell tiles can split features at tile boundaries. - A generic “woody” classification does not reveal whether a feature is a forest, wildlife corridor, or isolated copse. - England’s more than 130,000 km² of high-resolution imagery created major computational demands for conventional raster-to-vector processing. ## Deep learning and vectorization - The team fine-tuned Remote Sensing Foundations’ Vision Transformer, pretrained on more than 300 million global satellite images. - Only about 247 km² of annotated British landscape data was available, so the pretrained model helped transfer broad visual knowledge to local conditions. - A dual-layer labeling system combined submeter imagery with 1-meter LiDAR to distinguish: - Ground-level boundaries such as fields and water - Above-ground features such as trees and walls - Geometry-merging algorithms reconnected features split across S2-cell borders. ## Classifying ecological features - The system used the **Polsby–Popper compactness score** to classify detected shapes by geometry. - Woodlands were defined as contiguous canopies at least 30 meters in diameter. - Woody patches included small copses and individual trees. - Linear woody features, including hedgerows and corridors, were identified using a compactness score below 0.5. - This classification turns raw detections into an inventory tied more closely to ecological function. ## Scaling the analysis - Google Earth Engine processed thousands of S2 cells in parallel. - This made it possible to generate vector geometries for millions of features across England without overwhelming traditional systems. - The resulting dataset is intended for landowners, conservationists, scientists, and policymakers. ## Future applications - The researchers plan to apply the method to silvopasture and agrisilviculture. - High-precision monitoring could help detect conservation “leakage” beyond project boundaries. - Open access to the data could support restoration while protecting agricultural productivity and food security. The dataset provides a practical foundation for planning and measuring nature recovery on working lands, demonstrating how AI can translate overlooked landscape details into conservation decisions.
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