Nature disasters play a key role in shaping human-urban infrastructure interactions. Effective and efficient response to natural disasters is essential for building resilience and sustainable urban environment. Two types of information are usually the most necessary and difficult to gather in disaster response. The first information is about the disaster damage perception, which shows how badly people think that urban infrastructure has been damaged. The second information is geolocation awareness, which means how people’s whereabouts are made available. In this paper, we proposed a novel disaster mapping framework, namely CVDisaster, aiming at simultaneously addressing geolocalization and damage perception estimation using cross-view Street-View Imagery (SVI) and Very High-Resolution satellite imagery. CVDisaster consists of two cross-view models, where CVDisaster-Geoloc refers to a cross-view geolocalization model based on a contrastive learning objective with a Siamese ConvNeXt image encoder and CVDisaster-Est is a cross-view classification model based on a Coupled Global Context Vision Transformer (CGCViT). Taking Hurricane IAN as a case study, we evaluate the CVDisaster framework by creating a novel cross-view dataset (CVIAN) and conducting extensive experiments. As a result, we show that CVDisaster can achieve highly competitive performance (over 80% for geolocalization and 75% for damage perception estimation) with even limited fine-tuning efforts, which largely motivates future cross-view models and applications within a broader GeoAI research community. The data and code are publicly available at: https://github.com/tum-bgd/CVDisaster.

中文翻译：

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使用街景和 VHR 卫星图像进行交叉视图地理定位和灾难制图：以伊恩飓风为例


自然灾害在塑造人类与城市基础设施的互动方面发挥着关键作用。有效和高效的自然灾害响应对于建立韧性和可持续的城市环境至关重要。在灾难响应中，通常有两种类型的信息是最必要且最难收集的。第一个信息是关于灾害损失感知的，它显示了人们认为城市基础设施受损的严重程度。第二种信息是地理位置感知，即如何提供人们的行踪。在本文中，我们提出了一种新的灾害制图框架，即 CVDisaster，旨在使用跨视图街景图像 （SVI） 和甚高分辨率卫星图像同时解决地理定位和损失感知估计问题。CVDisaster 由两个交叉视图模型组成，其中 CVDisaster-Geoloc 是指基于对比学习目标的交叉视图地理定位模型，带有孪生 ConvNeXt 图像编码器，CVDisaster-Est 是基于耦合全局上下文视觉转换器 （CGCViT） 的交叉视图分类模型。以飓风 IAN 为案例研究，我们通过创建新颖的交叉视图数据集 （CVIAN） 并进行大量实验来评估 CVDisaster 框架。结果，我们表明 CVDisaster 即使在有限的微调工作下也可以实现极具竞争力的性能（超过 80% 的地理定位和 75% 的损伤感知估计），这在很大程度上激励了更广泛的 GeoAI 研究社区中的未来交叉视图模型和应用程序。数据和代码可在以下网址公开获得：https://github.com/tum-bgd/CVDisaster。