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.

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@article{xu2025_2405.01607,
  title={ Deep Learning for Wildfire Risk Prediction: Integrating Remote Sensing and Environmental Data },
  author={ Zhengsen Xu and Jonathan Li and Sibo Cheng and Xue Rui and Yu Zhao and Hongjie He and Haiyan Guan and Aryan Sharma and Matthew Erxleben and Ryan Chang and Linlin Xu },
  journal={arXiv preprint arXiv:2405.01607},
  year={ 2025 }
}

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