Abstract
Climate change results in an increased probability of extreme weather events that put societies and businesses at risk on a global scale. Therefore, near real-time mapping of natural hazards is an emerging priority for the support of natural disaster relief, risk management, and informing governmental policy decisions. Recent methods to achieve near real-time mapping increasingly leverage deep learning (DL). However, DL-based approaches are designed for one specific task in a single geographic region based on specific frequency bands of satellite data. Therefore, DL models used to map specific natural hazards struggle with their generalization to other types of natural hazards in unseen regions. In this work, we propose a methodology to significantly improve the generalizability of DL natural hazards mappers based on pre-training on a suitable pre-task. Without access to any data from the target domain, we demonstrate this improved generalizability across four U-Net architectures for the segmentation of unseen natural hazards. Importantly, our method is invariant to geographic differences and differences in the type of frequency bands of satellite data. By leveraging characteristics of unlabeled images from the target domain that are publicly available, our approach is able to further improve the generalization behavior without fine-tuning. Thereby, our approach supports the development of foundation models for earth monitoring with the objective of directly segmenting unseen natural hazards across novel geographic regions given different sources of satellite imagery.
Abstract (translated)
气候变化导致极端天气事件的概率增加,在全球范围内威胁着社会和企业的生计。因此,实时绘制自然灾害地图已成为支持自然灾害救济、风险管理和政府政策决策的新优先事项。近年来,实现实时绘制的方法越来越依赖于深度学习(DL)。然而,基于DL的方法专为在单个地理区域中完成特定任务而设计,这些任务基于特定的卫星数据频率 Band 进行设计。因此,用于绘制特定自然灾害的DL模型在与未观测到的地区中推广其他类型自然灾害方面遇到了困难。在本文中,我们提出了一种方法,旨在通过预先训练来提高DL自然灾害地图模型的泛化能力,以用于未观测到自然灾害的分割。我们不需要访问目标领域的任何数据,而是利用公开可用的目标领域的未标记图像的特征,证明了这种改进的泛化能力的四U网络架构的分割。重要的是,我们的方法和地理差异和卫星数据频率 Band 类型的不同差异是不变的。通过利用目标领域的公开可用的未标记图像的特征,我们的方法能够进一步改善泛化行为,而无需微调。因此,我们的方法支持地球监测框架的开发,旨在直接分割 novel 地理区域中的未观测自然灾害,通过使用不同的卫星图像来源。
URL
https://arxiv.org/abs/2301.09318
