As Earth's climate changes, it is impacting disasters and extreme weather events across the planet. Record-breaking heat waves, drenching rainfalls, extreme wildfires, and widespread flooding during hurricanes are all becoming more frequent and more intense. Rapid and efficient response to disaster events is essential for climate resilience and sustainability. A key challenge in disaster response is to accurately and quickly identify disaster locations to support decision-making and resources allocation. In this paper, we propose a Probabilistic Cross-view Geolocalization approach, called ProbGLC, exploring new pathways towards generative location awareness for rapid disaster response. Herein, we combine probabilistic and deterministic geolocalization models into a unified framework to simultaneously enhance model explainability (via uncertainty quantification) and achieve state-of-the-art geolocalization performance. Designed for rapid diaster response, the ProbGLC is able to address cross-view geolocalization across multiple disaster events as well as to offer unique features of probabilistic distribution and localizability score. To evaluate the ProbGLC, we conduct extensive experiments on two cross-view disaster datasets (i.e., MultiIAN and SAGAINDisaster), consisting diverse cross-view imagery pairs of multiple disaster types (e.g., hurricanes, wildfires, floods, to tornadoes). Preliminary results confirms the superior geolocalization accuracy (i.e., 0.86 in Acc@1km and 0.97 in Acc@25km) and model explainability (i.e., via probabilistic distributions and localizability scores) of the proposed ProbGLC approach, highlighting the great potential of leveraging generative cross-view approach to facilitate location awareness for better and faster disaster response. The data and code is publicly available at https://github.com/bobleegogogo/ProbGLC

翻译：随着地球气候变化，全球范围内的灾害与极端天气事件正受到显著影响。破纪录的热浪、倾盆暴雨、极端野火以及飓风期间的广泛洪水正变得愈发频繁和剧烈。对灾害事件做出快速高效响应，对于气候韧性与可持续发展至关重要。灾害响应中的一个关键挑战在于准确快速地识别灾害位置，以支持决策制定与资源分配。本文提出一种概率跨视角地理定位方法，称为ProbGLC，探索面向快速灾害响应的生成式位置感知新途径。该方法将概率性与确定性地理定位模型结合到一个统一框架中，同时通过不确定性量化增强模型可解释性，并实现最先进的地理定位性能。为快速灾害响应而设计的ProbGLC能够处理多种灾害事件下的跨视角地理定位任务，并提供概率分布与可定位性评分等独特功能。为评估ProbGLC，我们在两个跨视角灾害数据集（即MultiIAN和SAGAINDisaster）上进行了广泛实验，这些数据集包含多种灾害类型（如飓风、野火、洪水、龙卷风）的多样化跨视角图像对。初步结果证实了所提ProbGLC方法优越的地理定位精度（即Acc@1km达0.86，Acc@25km达0.97）与模型可解释性（通过概率分布与可定位性评分），凸显了利用生成式跨视角方法提升位置感知能力，以实现更优更快灾害响应的巨大潜力。数据与代码已公开于https://github.com/bobleegogogo/ProbGLC