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- As rapidly changing climates defy forecasts grounded in traditional historical weather patterns
- Primary sector: AI Infrastructure
- Open the company page to keep the follow-up signal in view.
As rapidly changing climates defy forecasts grounded in traditional historical weather patterns, specialized geospatial artificial intelligence is becoming critical for assessing extreme risk. Quebec-based Geosapiens provides deep learning solutions to the insurance and municipal planning sectors, helping clients quantify risks associated with flooding and wildfires.
The core technical challenge lies in predicting events that exceed known data. Standard machine learning models are inherently bounded; using thirty years of historical rainfall data, a model can only reliably predict conditions similar to those 30 years ago. To estimate the intensity of a potential 100-year storm, Geosapiens’ methodology relies on distributional extrapolation, a process designed to project parameters and understand how data distributions change past their established range.
For predictive models to guide major infrastructure decisions, the focus must shift from replicating historical trends to quantifying and extrapolating past known data boundaries.
This approach also addresses systemic risks associated with general generative AI. The company notes that common deep learning models can suffer from 'hallucinations' because they lack control over the uncertainty of the input data. Geospatial AI aims to quantify this uncertainty, providing a more rigorous basis for high-stakes financial and infrastructural decisions.
The application of these tools differs significantly between sectors. For insurance carriers, the model’s immediate utility is calculating annual premiums based on next year's predicted risk. Conversely, municipal planning requires assessing long-term resilience, demanding an understanding of what a 100-year storm would mean in a future climate change scenario.
While Environment Canada plans to incorporate AI into its forecasts, experts caution that most current models are fundamentally reliant on historical data and may not accurately capture non-stationarity, the fact that physical mechanisms governing weather variables shift over time. For these tools to become reliable for long-term planning, they must develop robust mechanisms to understand how climate variability will change rather than simply predicting what has happened before.
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