Timely and accurate geolocalization of natural disasters is crucial for effective emergency response, which is foundational for risk mitigation and resilience development. Although social media texts have been widely used to recognize and resolve disaster geolocations, the implicit geoinformation in social media images remains largely underexplored. In this paper, we propose a novel large language model (LLM)-enhanced disaster geolocalization method that considers both explicit and implicit geoinformation from multimodal data. Based on the recognition of geolocations in disaster-related images and texts, geolocalization results were obtained by combining LLMs with map services. The selection of a geolocalization strategy depends on the available geoinformation modality and the presence of spatial relationships. A multimodal dataset of 1,000 images and 1,000 texts from the Hurricane Harvey Twitter dataset was constructed to evaluate geolocalization accuracy through error distance. The results demonstrated that the proposed method achieves significant improvements over baseline geocoding and toponym retrieval methods, with overall accuracies of 81.45%, 78.40%, 74.60%, 65.20%, and 44.95% within 161, 100, 50, 10, and 1 km, respectively. These findings confirm the potential of LLMs in enhancing geolocalization by considering implicit geoinformation from multimodal data for future disaster response and broader GeoAI applications.

中文翻译：

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LLM- 使用来自多模态数据的隐式地理信息增强灾害地理定位：以飓风哈维为例


及时准确地对自然灾害进行地理定位对于有效的应急响应至关重要，这是降低风险和发展复原力的基础。尽管社交媒体文本已被广泛用于识别和解决灾难地理位置，但社交媒体图像中的隐含地理信息在很大程度上仍未得到充分探索。在本文中，我们提出了一种新的大型语言模型 （）LLM 增强的灾难地理定位方法，该方法同时考虑了来自多模态数据的显式和隐式地理信息。基于对灾害相关图像和文本中地理位置的识别，结合LLMs地图服务获得地理定位结果。地理定位策略的选择取决于可用的地理信息模态和空间关系的存在。构建了一个包含来自飓风哈维 Twitter 数据集的 1000 张图像和 1000 篇文本的多模态数据集，以评估通过误差距离的地理定位精度。结果表明，所提方法较基线地理编码和地名检索方法取得了显著改进，在161、100、50、10和1 km范围内，总体准确率分别为81.45%、78.40%、74.60%、65.20%和44.95%。这些发现证实了通过考虑来自多模态数据的隐式地理信息来增强地理定位的潜力LLMs，以用于未来的灾难响应和更广泛的 GeoAI 应用。