## 1 Introduction
[PERSON] [4] proposed a novel framework to extract fine-grained location and water depth information from social media text and images, analyzing the spatiotemporal characteristics of the extracted information to enable large-scale situational awareness at the urban level. Previous social sensing data have predominantly been unimodal. The method proposed in this paper effectively integrates information from different modalities, enhancing the accuracy and comprehensiveness of disaster situation awareness. Additionally, it demonstrates a strong generalization capability, making it applicable to disaster data of various types and scales. This study proposes a multimodal social media-based framework for disaster situational awareness to identify social media content related to typhoon disasters. The framework aims to address the limitations of previous research, such as low spatiotemporal resolution of disaster damage distribution and high noise in social media data, by leveraging fine-grained spatiotemporal situational awareness. Compared to prior studies, this work achieves significant advancements in spatiotemporal resolution, data integration methods, and the characterization of dynamic spatiotemporal features. It offers a novel perspective on extracting socially-perceived information during disasters, enhancing the efficiency of spatiotemporal situational awareness throughout the disaster process. Transfer learning was employed to apply the mature Deeplab v3+ (Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation) image segmentation algorithm to extract disaster damage information from Typhoon \"Haikui\" images on Weibo. For disaster damage information that cannot be extracted from social media images, such as power and water outages, the natural language processing model ERNIE (Enhanced Representation through Knowledge Integration) is used to extract the information from text and quickly generate disaster maps. This framework offers complementary views of disaster situational awareness, capitalizing on the advantages of social perception from social media data to enhance the accuracy and efficiency of disaster monitoring, providing valuable insights for decision-makers in the formulation and adjustment of emergency management strategies. The method proposed in this paper has a higher spatiotemporal resolution compared to previous studies. It extracts various disaster damage information from actual images and supplements the damage information with text, reflecting the dynamic perception process of typhoon disasters. Fuzhou, Fujian Province, a region significantly impacted by Typhoon \"Haikui\", was selected as a case study for analysis. The regional and local overview map of the study area are shown in Figure 1. The contributions of this research are threefold:  
* In terms of fine-grained temporal information extraction, traditional remote sensing imagery has long revisit cycles and coarse temporal resolution, making continuous monitoring challenging. In contrast, Sina Weibo social media data can achieve minute-level temporal resolution, enabling minute-by-minute situational awareness of disasters, thereby significantly improving temporal granularity. * For the fine-grained spatial information extraction, conventional observation stations are costly to construct and limited in number, making it difficult to capture spatial heterogeneity. By extracting disaster damage characteristics from real images and utilizing multi-dimensional data fields such as text, images, and geolocation, this approach identifies damage locations that are often missed by disaster relief agencies. This improves spatial resolution, providing real-time, on-site information about disaster damage, and helping decision-makers gain a more comprehensive understanding of the extent and severity of the disaster-affected areas. * For the coarse-grained spatiotemporal information extraction, the proposed integrated method offers a detailed reflection of the dynamic evolution of typhoon disaster situations.