Advancing Wildfire Risk Prediction via Morphology-Aware Curriculum Contrastive Learning
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Contrastive LearningSimilar Papers 제목 키워드 기반
Uncertainty-Aware Deep Learning for Wildfire Danger Forecasting
Wildfires are among the most severe natural hazards, posing a significant threat to both humans and natural ecosystems. The growing risk of wildfires increases the demand for forecasting models that are not only accurate…
Boundary-Aware Uncertainty Quantification for Wildfire Spread Prediction
Reliable wildfire spread prediction is vital for risk-aware emergency planning, yet most deep learning models lack principled uncertainty quantification (UQ). Further, for boundary-sensitive cases like wildfire spread, e…
FireScope: Wildfire Risk Raster Prediction with a Chain-of-Thought Oracle
Predicting wildfire risk is a reasoning-intensive spatial problem that requires the integration of visual, climatic, and geographic factors to infer continuous risk maps. Existing methods lack the causal reasoning and mu…
Reinforcement LearningTrustworthy Data-Driven Wildfire Risk Prediction and Understanding in Western Canada
In recent decades, the intensification of wildfire activity in western Canada has resulted in substantial socio-economic and environmental losses. Accurate wildfire risk prediction is hindered by the intrinsic stochastic…
Spatial Uncertainty Quantification in Wildfire Forecasting for Climate-Resilient Emergency Planning
Climate change is intensifying wildfire risks globally, making reliable forecasting critical for adaptation strategies. While machine learning shows promise for wildfire prediction from Earth observation data, current ap…
Decision Making