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.

Happy to help. This is the official methods description for the Canadian gov's FM3 data, it's probably the best place to start although the details are mostly covered in much longer publications that require some digging: https://cwfis.cfs.nrcan.gc.ca/background/dsm/fm3