RISK INTELLIGENCE
Deforestation forecasting powered by AI & Machine Learning
We build forecasting systems that predict where forest loss is most likely to occur within the next year - at 30m resolution. Organisations use our risk maps to prioritise patrols, allocate conservation resources, defend carbon baselines, and meet EUDR due-diligence requirements.
The Problem:
What we provide
Deforestation Risk Forecasting: Using state-of-the-art AI models, we predict where deforestation is most likely to occur within the next year. Our models analyze multispectral satellite imagery and historical forest loss data to identify emerging deforestation threats, including illegal logging, mining, and agricultural expansion.
Spatial Risk Mapping: Our team develops detailed risk maps that indicate the probability of forest loss for each pixel (~30m resolution). This enables clients to prioritize areas for conservation efforts and optimize resource allocation, whether protecting biodiversity hotspots or preventing illegal activities in remote regions.
Customizable Monitoring Solutions: Our system is designed to be scalable and adaptable to any tropical forest region globally. We work with governments, NGOs, and private organizations to tailor monitoring systems based on specific environmental challenges, from rapid deforestation fronts to gradual forest degradation.
Actionable Insights for Policy and Enforcement: Our predictive models not only provide alerts for near-term deforestation but also help inform long-term conservation strategies by identifying key drivers of deforestation, such as proximity to new infrastructure or areas of agricultural pressure.
Always rooted in the latest science
Read more in the peer reviewed article:
Ball, J. G. C., Petrova, K., Coomes, D. A., & Flaxman, S. (2022). Using deep convolutional neural networks to forecast spatial patterns of Amazonian deforestation. Methods in Ecology and Evolution, 13(11), 2622-2634.