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ARXIV:2605.15894 · WILDFIRE SMOKE CLASSIFICATION · SUBMITTED 18 MAY · 20:28 UTC · FRESHNESS STALE
ARXIV:2605.15894WILDFIRE SMOKE CLASSIFICATIONSUBMITTED 18 MAY · 20:28 UTCFRESHNESS STALERanjith Chodavarapu · arXiv
An uncertainty-aware model for wildfire smoke density classification from satellite imagery, providing confidence measures for emergency response.
Opportunity summary
Pain An uncertainty-aware model for wildfire smoke density classification from satellite imagery, providing confidence measures for emergency response.
Evidence 0 refs | 3 sources | 50% coverage
Blocker Evidence unverified
An uncertainty-aware model for wildfire smoke density classification from satellite imagery, providing confidence measures for emergency response. Existing deep learning approaches treat smoke detection as a binary task, producing point estimates without any measure…
Rapid and accurate wildfire smoke severity assessment from satellite images is essential for emergency response, air quality modeling, and human health risk management. Existing deep learning approaches treat smoke detection as a binary task,…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Evaluated on 16,298 real satellite patches derived from the Wildfire Detection dataset, our model achieves 93.8% weighted test accuracy (91.1% unweighted) with ECE=0.0274. Code…
Wildfire Smoke Classification moved forward this cycle; last verified May 2026. Public score 7.0/10. Production flags indicate code availability.
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An uncertainty-aware model for wildfire smoke density classification from satellite imagery, providing confidence measures for emergency response.
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Paper Pack
10.48550/arXiv.2605.15894An uncertainty-aware model for wildfire smoke density classification from satellite imagery, providing confidence measures for emergency response.
Abstract
Rapid and accurate wildfire smoke severity assessment from satellite images is essential for emergency response, air quality modeling, and human health risk management. Existing deep learning approaches treat smoke detection as a binary task, producing point estimates without any measure of prediction confidence. We propose a probabilistic framework to categorize a satellite patch into Light, Moderate, and Heavy severity classes and to provide decomposed epistemic and aleatoric uncertainty in a single forward pass. Our architecture uses the backbone of a pre-trained EfficientNet-B3 and a CBAM module with an evidential deep learning head that predicts Dirichlet concentration parameters, directly estimating vacuity (epistemic) and dissonance (aleatoric) without Monte Carlo sampling. Evaluated on 16,298 real satellite patches derived from the Wildfire Detection dataset, our model achieves 93.8% weighted test accuracy (91.1% unweighted) with ECE=0.0274. Selective prediction retaining the most certain 50% of patches achieves 96.7% accuracy. As image quality degrades, uncertainty increases monotonically, and vacuity is a practical scan quality measure. The Moderate class represents transitional smoke conditions that exhibit the highest epistemic uncertainty (mean vacuity = 0.187), confirming the model correctly identifies ambiguous smoke boundary regions. CBAM spatial attention maps localize to structurally distinctive scene regions, and t-SNE demonstrates the clear cluster separation of Light and Heavy smoke.
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PROBLEM
An uncertainty-aware model for wildfire smoke density classification from satellite imagery, providing confidence measures for emergency response. Existing deep learning approaches treat smoke detection as a binary task, producing point estimates without any measure of predictio...
METHOD
Rapid and accurate wildfire smoke severity assessment from satellite images is essential for emergency response, air quality modeling, and human health risk management. Existing deep learning approaches treat smoke detection as a binary task, producing point estimates without an...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Evaluated on 16,298 real satellite patches derived from the Wildfire Detection dataset, our model achieves 93.8% weighted test accuracy (91.1% unweighted) with ECE=0.0274. Code availability is flagged in...
WHY NOW
Wildfire Smoke Classification moved forward this cycle; last verified May 2026. Public score 7.0/10. Production flags indicate code availability.
Abstract-backed public claims while anchored extraction refreshes.
An uncertainty-aware model for wildfire smoke density classification from satellite imagery, providing confidence measures for emergency response. Existing deep learning approaches treat smoke detection as a binary task, producing point estimates without any measure of prediction confidence.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Rapid and accurate wildfire smoke severity assessment from satellite images is essential for emergency response, air quality modeling, and human health risk management. Existing deep learning approaches treat smoke detection as a binary task, producing point estimates without any measure of prediction confidence.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Evaluated on 16,298 real satellite patches derived from the Wildfire Detection dataset, our model achieves 93.8% weighted test accuracy (91.1% unweighted) with ECE=0.0274. Code availability is flagged in the production record; the public repository link still needs proof alignment.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Wildfire Smoke Classification moved forward this cycle; last verified May 2026. Public score 7.0/10. Production flags indicate code availability.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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An uncertainty-aware model for wildfire smoke density classification from satellite imagery, providing confidence measures for emergency response.
Segment
Wildfire Smoke Classification
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