提出概率化跨视角定位方法，提升灾情位置识别精度与可解释性。
Towards Generative Location Awareness for Disaster Response: A Probabilistic Cross-view Geolocalization Approach
- 融合概率与确定性模型，统一框架实现定位与不确定性量化。
- 在多灾种数据集上达0.86@1km与0.97@25km准确率。
- 适合应急响应、遥感分析人员快速定位灾害地点使用。
随着气候变化加剧，极端天气事件频发且强度上升，包括创纪录的热浪、暴雨、野火及飓风引发的大范围洪涝。快速高效的灾情响应对气候韧性与可持续发展至关重要。灾情定位的准确性与时效性是决策与资源调配的关键挑战。本文提出一种概率化跨视角地理定位方法ProbGLC，探索生成式位置感知在灾情响应中的新路径。该方法将概率与确定性定位模型整合为统一框架，在提升定位性能的同时增强模型可解释性（通过不确定性量化）。专为快速响应设计，ProbGLC可处理多种灾情事件的跨视角图像匹配，并输出概率分布与可定位性评分。在两个跨视角灾情数据集MultiIAN与SAGAINDisaster上进行大量实验，涵盖飓风、野火、洪水、龙卷风等多类灾害图像对。初步结果表明，ProbGLC在定位精度（Acc@1km=0.86，Acc@25km=0.97）与可解释性方面均表现优异，验证了生成式跨视角方法在提升灾情位置感知能力上的巨大潜力。代码与数据已公开于https://github.com/bobleegogogo/ProbGLC。
原文摘要 · Abstract (English)
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
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