Advancing Wildfire Risk Prediction via Morphology-Aware Curriculum Contrastive Learning

2025-07-23

AI Summary

Wildfires increasingly exacerbate ecological degradation and public health risks; however, their extreme sparsity and the high-dimensional spatiotemporal complexity of associated data severely limit the performance of existing deep learning models, while reliance on costly meteorological inputs hinders real-time model updating. To address these challenges, we propose a morphology-aware curriculum contrastive learning framework. First, we design a geography-informed curriculum learning strategy that dynamically adjusts sample difficulty to mitigate regional heterogeneity. Second, we introduce a contrastive sampling mechanism grounded in prior knowledge—including topography and vegetation—to enhance representation learning for rare fire events. Third, we fuse multi-source meteorological and remote sensing time-series data to improve embedding quality while reducing annotation dependency. Experiments demonstrate that our method significantly reduces computational overhead without compromising prediction accuracy, enabling high-frequency model updates and fine-grained regional adaptation.

Technology Category

• Machine Learning: Multimodal Learning
• Computer Vision: Multi-modal Vision
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

Wildfires significantly impact natural ecosystems and human health, leading to biodiversity loss, increased hydrogeological risks, and elevated emissions of toxic substances. Climate change exacerbates these effects, particularly in regions with rising temperatures and prolonged dry periods, such as the Mediterranean. This requires the development of advanced risk management strategies that utilize state-of-the-art technologies. However, in this context, the data show a bias toward an imbalanced setting, where the incidence of wildfire events is significantly lower than typical situations. This imbalance, coupled with the inherent complexity of high-dimensional spatio-temporal data, poses significant challenges for training deep learning architectures. Moreover, since precise wildfire predictions depend mainly on weather data, finding a way to reduce computational costs to enable more frequent updates using the latest weather forecasts would be beneficial. This paper investigates how adopting a contrastive framework can address these challenges through enhanced latent representations for the patch's dynamic features. We thus introduce a new morphology-based curriculum contrastive learning that mitigates issues associated with diverse regional characteristics and enables the use of smaller patch sizes without compromising performance. An experimental analysis is performed to validate the effectiveness of the proposed modeling strategies.

Problem

• Predicting wildfire risk with imbalanced data and high-dimensional spatio-temporal complexity
• Reducing computational costs for frequent wildfire updates using weather data
• Addressing regional variability in wildfire characteristics via contrastive learning

Innovation

• Morphology-based curriculum contrastive learning
• Enhanced latent representations for dynamic features
• Smaller patch sizes without performance loss