arXiv Machine Learning

Data-Driven Fire-Zone Segmentation for Improved Short-Term Wildfire Prediction

arXiv:2608. 07472v1 Announce Type: new Abstract: Wildfire prediction models typically discretize study areas into uniform grids, ignoring the heterogeneous spatial distribution of ignitions.

arXiv Machine Learning

Jul 21

Spectral-Morphological Attention U-Net: An Efficient Network for Active Wildfire Detection

arXiv:2607. 16472v1 Announce Type: cross Abstract: Over the past decades, the frequency of global wildfires has been increasing steadily.

By Yugong Zeng, Jonathan Wu

computer-vision benchmarks

arXiv Machine Learning

Jun 11

Neural-Parameterized Cellular Automata for Wildfire Spread

arXiv:2606. 11676v1 Announce Type: cross Abstract: Traditional wildfire models rely on rigid, low-dimensional parameters and static fuel maps, frequently underpredicting fire spread.

By Maksym Zhenirovskyy, Ion Matei, Rohit Vuppala, Takuya Kurihana, Hon Yung Wonga

safety

arXiv Machine Learning

Aug 7

Evaluating Machine Learning Models for Post-Wildfire Debris-Flow Prediction

arXiv:2608. 05265v1 Announce Type: new Abstract: Prediction of post-wildfire debris flows is critical for mitigating hazards to communities, infrastructure, and resources during intense rainfall in recently burned areas.

By Quinn Ledingham, Zhengsen Xu, Yimin Zhu, Zack Dewis, Mabel Heffring, Saeid Taleghanidoozdoozan, Motasem Alkayid, Megan Greenwood, Lincoln Linlin Xu

benchmarks safety

arXiv AI

Jun 18

IOAH3: Importance-Driven Adaptive Spatial Partitioning

arXiv:2606. 18280v1 Announce Type: cross Abstract: We present IOAH3 (Importance-Oriented Adaptive H3 partitioning), a computational method for constructing data-driven spatial partitions of geo-referenced observation domains.

By Ehsaneddin Jalilian

arXiv Machine Learning

Jun 5

Data-efficient flood depth prediction through domain-aware coreset selection and tabular foundation models

arXiv:2606. 05265v1 Announce Type: new Abstract: Near-real-time flood depth prediction demands surrogate models that are accurate, fast, and transferable across watersheds.

By Lipai Huang, Adithi Srinath, Manas Singh, Junwei Ma, Ali Mostafavi

arXiv AI

Aug 11

Dimensional Balance Improves Large Scale Spatiotemporal Prediction Performance

arXiv:2605. 18793v2 Announce Type: replace-cross Abstract: Accurate spatiotemporal pattern analysis is critical in fields such as urban traffic, meteorology, and public health monitoring.

By Jing Chen, Shixiang Pan, Yujie Fan, Haocheng Ye, Haitao Xu, Wenqiang Xu

rag safety

arXiv AI

Aug 6

Risk Is Not the Target: A Monotonic Framework for Evaluating Wildfire Operational Risk Signals

arXiv:2607. 21597v2 Announce Type: replace Abstract: Evaluating wildfire risk systems using standard machine-learning metrics such as F1-score or IoU is fundamentally flawed: these metrics assess event prediction accuracy, not the operational coherence of a continuous risk signal.

By Nicolas Caron, Christophe Guyeux, Hassan Noura, Maxime Coulmeau, Benjamin Aynes

llms agents

arXiv Machine Learning

Jun 19

Integrating national forest inventory, airborne lidar, and satellite imagery for wall-to-wall mapping of forest structure with computer vision

arXiv:2606. 20291v1 Announce Type: new Abstract: Remote sensing is increasingly relied upon to deliver actionable science for forest and wildfire risk management across large landscapes.

By Luke J. Zachmann, David D. Diaz, Vincent A. Landau, Chelsey Walden-Schreiner, Tony Chang, Nathan E. Rutenbeck, Katharyn A. Duffy, Kiarie Ndegwa, Andreas Gros, Scott Conway, Guy Bayes

computer-vision

arXiv Machine Learning

5d ago

Evaluating AlphaEarth Foundations Embeddings for Wildfire Susceptibility Mapping

arXiv:2608. 12663v1 Announce Type: cross Abstract: Wildfire susceptibility mapping typically relies on physical variables assembled from multiple remote-sensing, climate, and geospatial products.

By Yuan Zhuang, Sanaa Hobeichi, Peng Shi, Fei Huang

rag

arXiv Machine Learning

Jul 10

Evaluating the Generalizability of Foundation Models for Extreme Environmental Events: Case Study of California Wildfire PM2.5

arXiv:2607. 07951v1 Announce Type: new Abstract: Wildfire smoke events produce extreme PM$_{2.

By Yongcan Huang, Li Jiang, Ze Yu Liu

llms fine-tuning benchmarks

arXiv Machine Learning

Jun 25

FDN: Interpretable Spatiotemporal Forecasting with Future Decomposition Networks

arXiv:2606. 25201v1 Announce Type: new Abstract: Spatiotemporal systems comprise a collection of spatially distributed yet interdependent entities each generating unique dynamic signals.

By Nicholas Majeske, Ariful Azad

benchmarks safety

arXiv Machine Learning

Jul 14

Manifold Constrained Conformal Prediction for Spatial Events

arXiv:2607. 10008v1 Announce Type: cross Abstract: We introduce a new conformal prediction method that constructs calibrated prediction sets over collections of spatial events, such as tropical cyclone genesis and earthquake locations.

By Collin Nill, Trevor Harris, Jason Adams

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