Hedgerows, stone walls, and small woodland copses are the hidden connective tissue of England's countryside. They shelter wildlife, store carbon, and stitch together fragmented habitats. But they are also, from the perspective of most satellites, essentially invisible. Standard remote sensing resolution is simply too coarse to detect features that might be only a few meters wide. Google Research just changed that.
Google Research has released the Vectorized Farmscapes 2020 dataset, which transforms high-resolution pixel maps of fine-scale ecological features into an actionable vector inventory of hedgerows, stone walls, and copses. The dataset is available now, free of charge, through Google Earth Engine, and is aimed at landowners, conservationists, ecologists, and policymakers who need precise, plannable data rather than raw probability maps.
The problem with pixels
Fine-scale woody features like hedgerows and shelterbelts woven among farms 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. This is not just a data gap -- it is a policy gap. Without knowing where these features are, governments cannot reward farmers for maintaining them, and carbon markets cannot credit them.
Google 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. But that earlier release was a raster dataset -- essentially a grid of pixels, each carrying a probability score. While the initial raster format was a step forward in detection, real-world applications for landscape restoration and carbon accounting require more than pixels. You cannot feed a probability heatmap into a planning tool or a carbon registry. You need shapes, boundaries, and categories.
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