From pixels to planning: Earth AI for nature restoration

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.

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.

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.

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.