Data gaps and poor metrics undermine AI’s role in global wildfire crisis

Commonly used models often excel on benchmark datasets but do not generalize across regions or varying wildfire conditions. The review points to data imbalance and access limitations as central problems, most models are trained on data from limited geographies and seasons, making them unreliable elsewhere.

A new review calls for a fundamental rethink of how artificial intelligence is developed and deployed for wildfire management. It provides a comprehensive assessment of how AI has been applied to mapping, predicting, detecting, simulating, and assessing the impact of wildfires, while exposing the critical gap between promising laboratory results and operational success in the field.

The study, titled “AI for Wildfire Management: From Prediction to Detection, Simulation, and Impact Analysis — Bridging Lab Metrics and Real-World Validation,” was published in AI. 

Identifying gaps between lab results and field needs

Many AI models celebrated in research papers fail when deployed in real-world wildfire scenarios. A major reason is the mismatch between laboratory metrics and field requirements.

Commonly used models often excel on benchmark datasets but do not generalize across regions or varying wildfire conditions. The review points to data imbalance and access limitations as central problems, most models are trained on data from limited geographies and seasons, making them unreliable elsewhere.

Another challenge is output incompatibility. Predictions that look good in academic evaluations often do not align with the practical formats and needs of firefighters, emergency managers, or environmental agencies. The authors stress that improving generalization and usability is critical for AI to become a trusted tool in crisis response.

Technical recommendations for building reliable AI

The study provides specific guidance for improving model robustness and ensuring fair evaluation.

Metric selection: The authors recommend using PR-AUC, F1-score, and Expected Calibration Error (ECE) for highly imbalanced detection problems, rather than relying on accuracy alone. For mapping fire extent, Intersection-over-Union (IoU) and Dice coefficients are suggested, while RMSE, MAE, and R² remain appropriate for regression tasks such as predicting burned areas or emissions.

Data practices: To reduce bias, the review advises generating non-fire points using spatially informed methods rather than simplistic or rule-based sampling. It also encourages testing models on multiple datasets and across regions to identify weaknesses in spatial generalization.

Target modeling: The authors suggest adding ordinal classification for risk levels, which respects the natural ordering of fire severity and reduces misleading errors.

Damage and health impact assessment: The paper urges combining satellite-based burned-area and severity analyses with air-quality and smoke-exposure modeling to estimate downstream health risks, an aspect often overlooked in traditional wildfire AI research.

A roadmap for moving from research to operations

The review lays out a deployment checklist to help bridge the gap between experimental success and operational value. The checklist addresses cost, latency, data availability, required expertise, and integration with existing decision-support systems.

According to the authors, compatibility with emergency response tools and workflows is just as important as algorithmic accuracy. They call for closer collaboration among AI developers, field agencies, and policy-makers to ensure that outputs, such as risk maps or alerts, are in the right formats and timeframes for immediate action.

Future directions include the use of digital twins of ecosystems for real-time monitoring, 5G-enabled communication systems for rapid data exchange in the field, and combining AI with optimization models to improve resource allocation and response planning during active fire events.

Shaping future research and policy

The review warns that over-reliance on lab benchmarks risks wasting resources and slowing progress on climate-driven wildfire challenges. It argues that the next generation of wildfire AI research must prioritize:

• Cross-regional benchmarking to improve model transferability.
• Transparent reporting of data sources and biases.
• Field-ready output formats that match decision-makers’ needs.
• Uncertainty calibration and explainability to build trust among end-users.

The authors also advocate for better access to diverse, high-quality datasets, which they describe as a key prerequisite for both model development and equitable deployment in regions most vulnerable to wildfires.