Forecasting the future of forests with AI: From counting losses to predicting risk (opens in new tab)

Research from Google DeepMind and Google Research introduces ForestCast, a deep learning-based framework designed to transition forest management from retrospective loss monitoring to proactive risk forecasting. By utilizing vision transformers and pure satellite data, the team has developed a scalable method to predict future deforestation that matches or exceeds the accuracy of traditional models dependent on inconsistent manual inputs. This approach provides a repeatable, future-proof benchmark for protecting biodiversity and mitigating climate change on a global scale.

Limitations of Traditional Forecasting

• Existing state-of-the-art models rely on specialized geospatial maps, such as infrastructure development, road networks, and regional economic indicators.
• These traditional inputs are often "patchy" and inconsistent across different countries, requiring manual assembly that is difficult to replicate globally.
• Manual data sources are not future-proof; they tend to go out of date quickly with no guarantee of regular updates, unlike continuous satellite streams.

A Scalable Pure-Satellite Architecture

• The ForestCast model adopts a "pure satellite" approach, using only raw inputs from Landsat and Sentinel-2 satellites.
• The architecture is built on vision transformers (ViTs) that process an entire tile of pixels in a single pass to capture critical spatial context and landscape-level trends.
• The model incorporates a satellite-derived "change history" layer, which identifies previously deforested pixels and the specific year the loss occurred.
• By avoiding socio-political or infrastructure maps, the method can be applied consistently to any region on Earth, allowing for meaningful cross-regional comparisons.

Key Findings and Benchmark Release

• Research indicates that "change history" is the most information-dense input; a model trained on this data alone performs almost as well as those using raw multi-spectral data.
• The model successfully predicts tile-to-tile variation in deforestation amounts and identifies the specific pixels most likely to be cleared next.
• Google has released the training and evaluation data as a public benchmark dataset, focusing initially on Southeast Asia to allow the machine learning community to verify and improve upon the results.

The release of ForestCast provides a template for scaling predictive modeling to Latin America, Africa, and boreal latitudes. Conservationists and policymakers should utilize these forecasting tools to move beyond counting historical losses and instead direct resources toward "frontline" areas where the model identifies imminent risk of habitat conversion.