Belief-Aware Scheduling for Predictive Wildfire Hazard Mapping under Sparse-Window Telemetry

2026-06-05

AI Summary

This study addresses the challenge of jointly scheduling regional sensing, representation, and transmission under telemetry bandwidth constraints—modeled as sparse observation windows—to enable accurate prediction of future wildfire risk maps at the receiver. The problem is formulated as a partially observable sequential resource allocation task. To explicitly link scheduling decisions with predictive performance, the authors propose a prediction-oriented structured belief mechanism grounded in the input requirements of the forward operator. Evaluated in a physics-calibrated synthetic environment using a lightweight cross-regional attention encoder (only 40k parameters), the approach outperforms baseline methods by 28% and 11% on default and structured landscapes, respectively. Notably, deeper Transformer architectures yield no average loss improvement and exhibit higher training variance, underscoring the efficacy of the proposed lightweight design.

Abstract

An edge node monitoring a wildfire observes more than a duty-limited or windowed down-link can carry. The receiver must predict the H-step-ahead hazard map from whatever the link delivers. We argue the operative design problem is not which neural architecture to use but how to derive a structured belief sufficient for the receiver's prediction task and maintain it through a scheduler that anticipates future transmission opportunities. We formalize this as a partially observed sequential allocation problem with three coupled per-region action axes (sensing, representation, transmission), and derive each component of the structured belief from the H-step forward operator's input requirements. Identifying these mechanisms requires independent control over the window period P, per-window capacity C, predictive horizon H, and fuel composition, which is not separable in real-landscape data; we therefore evaluate on a physics-calibrated synthetic environment. Three empirical observations support the principle: the gap between a non-myopic activity-paced reference and uniform pacing is unimodal in window-period sparsity, peaking at intermediate spacing; ablating the structured belief, the dominant operative component flips between a default landscape (temporal staleness) and a structured landscape (static-risk prior), while the per-cell intensity belief is redundant in both; and a 40 k-parameter lightweight cross-region attention encoder exceeds the FAIR activity-paced reference by ~28% on the default landscape and ~11% on the structured landscape. A deeper Transformer encoder does not improve over the lightweight encoder in mean predictive loss and exhibits higher training-seed variance. Within this task class and regime, a modest architectural inductive bias suffices when the belief and the scheduling problem are correctly posed.

Problem

• wildfire hazard mapping
• sparse-window telemetry
• belief-aware scheduling
• predictive modeling
• edge sensing

Innovation

• belief-aware scheduling
• structured belief
• predictive wildfire mapping
• sparse-window telemetry
• sequential resource allocation