通过形态感知课程对比学习推进野火风险预测
Advancing Wildfire Risk Prediction via Morphology-Aware Curriculum Contrastive Learning
AI总结：
本文提出一种基于形态的课程对比学习方法，通过增强潜在表示来解决野火风险预测中的数据不平衡和高维时空数据复杂性问题。
摘要：
野火对自然生态系统和人类健康产生重大影响，导致生物多样性丧失、水文地质风险增加以及有毒物质排放增加。气候变化加剧了这些影响，特别是在温度上升和干旱期延长的地区，如地中海地区。这要求开发出利用最新技术的先进风险管理办法。然而，在这种背景下，数据呈现出不平衡的设置，即野火事件的发生率显著低于典型情况。这种不平衡，加上高维时空数据的内在复杂性，对训练深度学习架构提出了重大挑战。此外，由于精确的野火预测主要依赖于天气数据，找到一种方法来减少计算成本，以利用最新天气预报进行更频繁的更新将是有益的。本文探讨了采用对比框架如何通过增强的潜在表示来解决这些挑战，通过动态特征的潜在表示来增强。我们因此引入了一种新的基于形态的课程对比学习，以减轻与不同地区特征相关的难题，并使使用较小的块大小而不影响性能成为可能。进行了实验分析以验证所提出建模策略的有效性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
