We do some similar work with hotspot analysis but (as a Canadian company) are more focused on Canadian data where the government already does a fair bit of false positive detection and filtering. It generally gives pretty clean data and we can scrub historical data over time like this: https://imgur.com/a/gCJGzqd

The dataset includes US coverage but it's not filtered the same way and FAR more noisy, so I appreciate efforts like this. We haven't got there yet but if you were looking for something deterministic and automatable the Canadian gov's process is potentially worth learning about.

They also produce perimeter estimates based on the hotspots which we can extract and put into a physics-based fire growth model like Prometheus or FARSITE to estimate future fire behaviour based on forecasted weather. This gives very actionable and deterministic estimates of future fire behaviour. We also have worked on a risk model that determines the likelihood of that future fire growth interacting with various assets on the landscape (urban interface areas, power lines, fuel pipelines, forest inventory, etc) and calls out high risk areas. One thing we've been wondering if where LLMs fit into any of this (if at all) so appreciate seeing what others are doing.

Thanks, this is really helpful. That filtering/perimeter pipeline is exactly the kind of deterministic path I'm interested in learning from, especially for pushing more of the false-positive reduction upstream before the model gets involved at all.

My take so far is that models seem most useful in the contextual triage step and in synthesizing multiple sources into a structured assessment. But most of the system around that is and should be deterministic.

The physics-based approach you're describing makes a lot of sense to me for spread prediction - different tool for a different part of the problem.

If there's a public writeup on the filtering process you'd recommend, I'd love to take a look.