Science at Earthshot

Applying AI, machine learning, and ecosystem physics modelling to global ecosystem restoration.

Research Snapshots

ARR Carbon Projections

Our platform for predicting restoration outcomes leverages (1) an expansive database of ecological outcomes worldwide; (2) the integration of ground observations with remote sensing through machine learning; (3) process understanding through land surface models and knowledge-guided machine learning, and (4) global scaling through automation.

Natural Risk Modeling

Fire, drought and disease threaten all ecosystems more than ever due to climate change. To quantify the likelihood of such natural risks to Earthshot projects, we use global models based on observations of historic impact, combined with Earth System Model projections of likely future climate change. Our approach helps accurately quantify the appropriate buffer pool, which directly improves project financing and quality.

REDD+ Scoping Automation in LandOS

Accurate deforestation and biomass baselines are essential for the development of robust REDD+ projects. The carbon verification process, however, can be long and costly. Our REDD+ Scoping automation allows for quick and simplified assessments that ensure project feasibility and quality.

Deforestation Risk Assessment Tool

Earthshot’s Deforestation Risk Tool uses multiple publicly available datasets based on satellite imagery to quantify land eligibility, deforestation baselines, and risk. Our approach generates a rigorous and detailed assessment that goes far beyond requirements for the most widely used greenhouse gas crediting programs. Integration with machine learning provides full automation, enabling us to evaluate global deforestation risk at scale.

Global Biomass History

We use space-based observations to improve our estimates of carbon stocks and biomass changes. Leveraging a suite of satellite observations, such as GEDI and Landsat, combined with a convolutional neural network, we track changes in aboveground biomass for every location on Earth, and every year.

Process Modeling

We use mathematical models that represent the complex process interactions within ecosystems, such as the Functionally Assembled Terrestrial Ecosystem Simulator (FATES). This allows us to predict intervention outcomes directly based on the processes that determine them. In FATES, trees of different species compete to determine the ultimate carbon uptake, biodiversity, water use, and biophysical feedbacks to the atmosphere.

Deep Forest: Visualizing Reforestation

Visualizing the negative consequences of climate change has a significant impact on people’s emotions, motivations, and behaviors. We do the opposite, allowing people to see the landscape our projects aspire to. Using new advances in generative adversarial networks, we directly visualize what an area could look like under reforestation. These real life images allow viewers to feel the impact of their actions, and are but the first step in leveraging AI to better connect people with the planet.

Open Science


Open source Python package to ingest data from database or similar data sources, perform necessary allometric scaling and transforms that prepare data, and generate carbon accumulation curves with the associated uncertainty.
“Now the world is recognizing the urgent need to do ecological restoration at scale, for hundreds of thousands of hectares in almost all ecosystems worldwide.

This is required by the Paris Agreements to meet our net zero goals, and requires a new way of thinking, and new science."
Dr Trevor Keenan
Chief Scientist
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