Skip to content
AI News HubLIVE
Original source2 min read

Boundary-Aware Uncertainty Quantification for Wildfire Spread Prediction

Summary

A new framework called Fire-Centered Evaluation Region (FCER) focuses uncertainty quantification on critical fire zones, comparing ensemble and distilled student models on wildfire spread prediction. The student model achieves comparable calibration and complementary uncertainty rankings in boundary-sensitive areas, pointing toward more operationally relevant evaluation.

SourcearXiv Computer VisionAuthor: Jonas V. Funk
Boundary-Aware Uncertainty Quantification for Wildfire Spread Prediction
Report an error

The correction channel is not available yet. You can copy the article reference below for later.

Correction instructions
Read article

[2605.03148] Boundary-Aware Uncertainty Quantification for Wildfire Spread Prediction

Skip to main content

Learn about arXiv becoming an independent nonprofit.

We gratefully acknowledge support from the Simons Foundation, member institutions, and all contributors. Donate

> cs > arXiv:2605.03148

Help | Advanced Search

All fields Title Author Abstract Comments Journal reference ACM classification MSC classification Report number arXiv identifier DOI ORCID arXiv author ID Help pages Full text

Search

GO

quick links

Login

Help Pages

About

-->

Computer Science > Computer Vision and Pattern Recognition

arXiv:2605.03148 (cs)

[Submitted on 4 May 2026]

Title:Boundary-Aware Uncertainty Quantification for Wildfire Spread Prediction

Authors:Jonas V. Funk

View a PDF of the paper titled Boundary-Aware Uncertainty Quantification for Wildfire Spread Prediction, by Jonas V. Funk

View PDF HTML (experimental)

Abstract:Reliable wildfire spread prediction is vital for risk-aware emergency planning, yet most deep learning models lack principled uncertainty quantification (UQ). Further, for boundary-sensitive cases like wildfire spread, evaluating models with global metrics alone is often insufficient. To shift the focus of UQ evaluation toward a more operationally relevant approach, the Fire-Centered Evaluation Region (FCER) framework is introduced as a spatially conditioned protocol to characterize UQ within critical fire zones. Using FCER, an Ensemble is compared against an distilled single-pass student model on the WildfireSpreadTS dataset. The student model demonstrates comparable calibration and complementary uncertainty ranking in boundary-relevant regimes. Code is available at https://github. com/jonasvilhofunk/WildfireUQ-FCER

Comments: 10 pages, 7 figures

Subjects:

Computer Vision and Pattern Recognition (cs.CV)

Cite as: arXiv:2605.03148 [cs.CV]

(or arXiv:2605.03148v1 [cs.CV] for this version)

https://doi.org/10.48550/arXiv.2605.03148

Focus to learn more

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Jonas Vilho Funk [view email] [v1] Mon, 4 May 2026 20:41:51 UTC (113 KB)

Full-text links:

Access Paper:

View a PDF of the paper titled Boundary-Aware Uncertainty Quantification for Wildfire Spread Prediction, by Jonas V. Funk

View PDF

HTML (experimental)

TeX Source

view license

Current browse context:

cs.CV

new | recent | 2026-05

Change to browse by:

cs

References & Citations

NASA ADS

Google Scholar

Semantic Scholar

export BibTeX citation Loading...

BibTeX formatted citation

×

loading...

Data provided by:

Bookmark

Bibliographic Tools

Bibliographic and Citation Tools

Bibliographic Explorer Toggle

Bibliographic Explorer (What is the Explorer?)

Connected Papers Toggle

Connected Papers (What is Connected Papers?)

Litmaps Toggle

Litmaps (What is Litmaps?)

scite.ai Toggle

scite Smart Citations (What are Smart Citations?)

Code, Data, Media

Code, Data and Media Associated with this Article

alphaXiv Toggle

alphaXiv (What is alphaXiv?)

Links to Code Toggle

CatalyzeX Code Finder for Papers (What is CatalyzeX?)

DagsHub Toggle

DagsHub (What is DagsHub?)

GotitPub Toggle

Gotit.pub (What is GotitPub?)

Huggingface Toggle

Hugging Face (What is Huggingface?)

ScienceCast Toggle

ScienceCast (What is ScienceCast?)

Demos

Demos

Replicate Toggle

Replicate (What is Replicate?)

Spaces Toggle

Hugging Face Spaces (What is Spaces?)

Spaces Toggle

TXYZ.AI (What is TXYZ.AI?)

Related Papers

Recommenders and Search Tools

Link to Influence Flower

Influence Flower (What are Influence Flowers?)

Core recommender toggle

CORE Recommender (What is CORE?)

Author

Venue

Institution

Topic

About arXivLabs

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)

About

Help

contact arXivClick here to contact arXiv Contact

subscribe to arXiv mailingsClick here to subscribe Subscribe

Copyright

Privacy Policy

Web Accessibility Assistance

arXiv Operational Status

Key points and analysis

Article intelligence

EngineersAdvanced

Key points

  • Most deep learning models lack principled uncertainty quantification for wildfire spread, especially at boundaries.
  • The FCER framework provides a spatially conditioned protocol to evaluate uncertainty in critical fire zones.
  • A distilled single-pass student model matches ensemble performance and offers complementary uncertainty information.
  • Code is publicly available for reproducibility.

Highlights and analysis are generated automatically and may contain errors. Check the original source.

More recent stories on this topic · Research