[Paper Review] Deep Learning for Wildfire Risk Prediction: Integrating Remote Sensing and Environmental Data

Zhengsen Xu, Jonathan Li+9 more|arXiv (Cornell University)|May 2, 2024

Fire effects on ecosystems4 citations

TL;DR

This paper presents a comprehensive review of deep learning methods for wildfire risk prediction by integrating remote sensing and environmental data. It evaluates data sources, preprocessing techniques, model architectures—including RNNs, CNNs, GNNs, and Transformers—and highlights the superiority of deep learning over traditional models, while identifying gaps in calibration, uncertainty quantification, and high-resolution spatiotemporal data use.

ABSTRACT

Wildfires pose a significant threat to ecosystems, wildlife, and human communities, leading to habitat destruction, pollutant emissions, and biodiversity loss. Accurate wildfire risk prediction is crucial for mitigating these impacts and safeguarding both environmental and human health. This paper provides a comprehensive review of wildfire risk prediction methodologies, with a particular focus on deep learning approaches combined with remote sensing. We begin by defining wildfire risk and summarizing the geographical distribution of related studies. In terms of data, we analyze key predictive features, including fuel characteristics, meteorological and climatic conditions, socioeconomic factors, topography, and hydrology, while also reviewing publicly available wildfire prediction datasets derived from remote sensing. Additionally, we emphasize the importance of feature collinearity assessment and model interpretability to improve the understanding of prediction outcomes. Regarding methodology, we classify deep learning models into three primary categories: time-series forecasting, image segmentation, and spatiotemporal prediction, and further discuss methods for converting model outputs into risk classifications or probability-adjusted predictions. Finally, we identify the key challenges and limitations of current wildfire-risk prediction models and outline several research opportunities. These include integrating diverse remote sensing data, developing multimodal models, designing more computationally efficient architectures, and incorporating cross-disciplinary methods--such as coupling with numerical weather-prediction models--to enhance the accuracy and robustness of wildfire-risk assessments.

Motivation & Objective

• To systematize the selection of input data, preprocessing workflows, and modeling approaches for wildfire risk prediction across diverse ecological zones.
• To evaluate the performance and limitations of statistical, machine learning, and deep learning models in predicting wildfire occurrence and risk.
• To identify critical research gaps in data quality, model calibration, uncertainty estimation, and the integration of 3D fuel and high-resolution time series data.
• To provide a structured framework for researchers to select appropriate data sources, preprocessing techniques, and model architectures based on study area characteristics and data availability.
• To advocate for the integration of deep learning with physical models to overcome current accuracy bottlenecks in wildfire prediction.

Proposed method

• Classifies input variables into five categories: fuel conditions, climate and meteorology, socio-economic factors, terrain and hydrology, and historical wildfire records.
• Reviews data preprocessing techniques tailored to varying spatiotemporal resolutions and data formats, including remote sensing indices and multi-source data fusion.
• Evaluates collinearity and feature importance using statistical and model-agnostic techniques such as variance inflation factors and permutation importance.
• Categorizes and analyzes deep learning models—RNNs (LSTM, GRU), CNNs, GNNs, GCNs, and Transformers—for temporal and spatial wildfire risk forecasting.
• Assesses model evaluation metrics, including AUC-ROC, Brier score, and calibration techniques like Platt scaling and temperature scaling.
• Proposes integration of deep learning with physical models to improve predictive accuracy and interpretability, especially in data-scarce regions.

Experimental results

Research questions

• RQ1 Which data sources and preprocessing methods are most effective for wildfire risk prediction across different ecological and land cover types?
• RQ2 How do different deep learning architectures (e.g., RNNs, CNNs, Transformers) compare in performance for spatiotemporal wildfire risk forecasting?
• RQ3 What are the key limitations in current model calibration and uncertainty quantification for wildfire prediction systems?
• RQ4 How can multi-source remote sensing and environmental data be optimally combined to improve fuel and fire behavior characterization?
• RQ5 What role can hybrid models combining deep learning with physical models play in overcoming accuracy and generalization limitations?

Key findings

• Deep learning models, particularly RNNs, CNNs, and Transformers, significantly outperform traditional statistical and machine learning models in wildfire risk prediction tasks.
• Most studies prioritize meteorological and climatic variables, followed by fuel and terrain data, with socio-economic and hydrological factors receiving less attention.
• Model calibration remains a critical gap—many models produce poorly calibrated probabilities, and techniques like Platt scaling and temperature scaling are underutilized in wildfire prediction.
• There is a lack of robust uncertainty quantification in current models, with few studies employing Bayesian or ensemble-based methods to communicate prediction confidence.
• Historical fire records derived from remote sensing are often inaccurate or incomplete, highlighting the need for improved data extraction and filtering techniques.
• Future research should focus on high-resolution, 3D fuel data, large-scale spatiotemporal modeling, and hybrid deep learning–physical modeling frameworks to enhance predictive accuracy and reliability.