Up-to-date information about an emergency is crucial for effective disaster management. However, severe restrictions impede the creation of spatiotemporal information by current remote sensing-based monitoring systems, especially at the beginning of a disaster. Multiple publications have shown promising results in complementing monitoring systems through spatiotemporal information extracted from social media data. However, various monitoring system criteria, such as near-real-time capabilities or applicability for different disaster types and use cases, have not yet been addressed. This paper presents an improved version of a recently proposed methodology to identify disaster-impacted areas (hot spots and cold spots) by combining semantic and geospatial machine learning methods. The process of identifying impacted areas is automated using semi-supervised topic models for various kinds of natural disasters. We validated the portability of our approach through experiments with multiple natural disasters and disaster types with differing characteristics, whereby one use case served to prove the near-real-time capability of our approach. We demonstrated the validity of the produced information by comparing the results with official authority datasets provided by the United States Geological Survey and the National Hurricane Centre. The validation shows that our approach produces reliable results that match the official authority datasets. Furthermore, the analysis result values are shown and compared to the outputs of the remote sensing-based Copernicus Emergency Management Service. The information derived from different sources can thus be considered to reliably detect disaster-impacted areas that were not detected by the Copernicus Emergency Management Service, particularly in densely populated cities.

有关紧急情况的最新信息对于有效的灾害管理至关重要。然而，严格的限制阻碍了当前基于遥感的监测系统创建时空信息，尤其是在灾难开始时。多个出版物通过从社交媒体数据中提取的时空信息，在补充监测系统方面显示出可喜的成果。然而，各种监控系统标准，例如近实时能力或对不同灾难类型和用例的适用性，尚未得到解决。本文介绍了最近提出的方法的改进版本，该方法通过结合语义和地理空间机器学习方法来识别受灾区域（热点和冷点）。使用针对各种自然灾害的半监督主题模型来自动识别受影响区域的过程。我们通过对多种自然灾害和具有不同特征的灾害类型的实验验证了我们方法的可移植性，其中一个用例证明了我们方法的近实时能力。我们通过将结果与美国地质调查局和国家飓风中心提供的官方权威数据集进行比较来证明所产生信息的有效性。验证表明，我们的方法产生了与官方权威数据集相匹配的可靠结果。此外，还显示了分析结果值，并与基于遥感的哥白尼应急管理服务的输出进行了比较。