Advancing Wildfire Risk Prediction via Morphology-Aware Curriculum Contrastive Learning

TL;DR

This paper introduces a morphology-aware curriculum contrastive learning approach to improve wildfire risk prediction by enhancing data representations, addressing data imbalance, and reducing computational costs for more frequent updates.

Contribution

It presents a novel contrastive learning framework that incorporates morphological information and curriculum strategies to handle regional diversity and data imbalance in wildfire prediction.

Findings

Improved latent representations for wildfire data.

Effective handling of regional morphological differences.

Reduced computational costs enabling frequent updates.

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.