Forest inventory maps
at species-level precision
Take full stock of your forest, down to individual tree crowns. We transform remote sensing imagery into … Applying the very latest in AI modeling techniques developed by forestmap’s CEO
Reactive monitoring is becoming a liability.
Regulators, registries, and buyers are no longer satisfied with evidence of damage already done. Staying reactive exposes you to credit invalidation, failed due diligence, and reputational risk — at the moment forest assets face the sharpest scrutiny in a decade.
EUDR enforcement is creating hard deadlines for geolocated, deforestation-free supply-chain evidence.
Carbon-market integrity frameworks increasingly require dynamic, forward-looking baselines — not static historical matching.
Investor and buyer diligence on nature-based assets is tightening; lagging indicators no longer pass review.
What you receive:
Combining deep learning models with large-scale Earth observation data, we transform satellite imagery into actionable risk insight. Here's how that translates into what you can actually do with it.
Spatial Risk Maps
Actionable Insights
Tailored Solutions
You will receive actionable risk maps at up to 30m resolution, so you can determine exactly where to prioritize areas for conservation efforts and optimize resource allocation — whether protecting biodiversity hotspots or preventing illegal activities in remote regions.
Annual Risk Forecasts
Where is deforestation most likely in the next 12 months? Our models learn from historical patterns and multi-year satellite imagery to predict emerging threats - including illegal logging, mining expansion, and agricultural pressure.
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.
Our system is scalable to any tropical forest region globally, and tailored to the risk profile that matters most. From the Amazon to urban forest in Germany, we provide governments, NGOs, and private organizations with monitoring systems tailored to their specific environmental challenges and needs.
Built for the people underwriting forest risk.
Carbon project developers & buyers — get ahead of reversal risk before it threatens a buffer pool or surfaces at audit.
Nature & biodiversity finance — price ecosystem assets on forward-looking risk instead of a backward-looking snapshot.
EUDR compliance teams — demonstrate risk-based due diligence across a full supply shed, not just a single plot.
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.