## 1 Introduction
Multimodal social media data have been increasingly used to enhance disaster damage assessment and situational awareness of natural disasters. In disaster scenarios, the use of location-based real-time social media posts in spatial decision support processes holds significant potential for resource allocation, identifying damaged areas, estimating damage severity, and determining evacuation or aid routes. Disaster management professionals and volunteers have validated the reliability and practicality of crowdsourced images for damage assessment [27]. Images and text offer strong complementarity; for instance, the severity of a disaster can be easily observed from images, while information about affected individuals can be more accurately extracted from text [28]. Rescue organizations also require concise situational awareness information, such as damage severity assessments. [PERSON] [29] trained an effective damage assessment classifier using web image data, developing a damage severity assessment model. [PERSON]'s experiments demonstrated that domain-specific fine-tuning of deep convolutional neural networks (CNNs) outperformed other advanced techniques for disaster damage assessment. [PERSON] [30] focused on detecting human and environmental damage in disaster impact detection. [PERSON] [31], in collaboration with volunteer response organizations, utilized image data shared on Twitter and applied deep neural network-based image processing techniques to identify disaster damage. After a disaster, rapid assessment and classification of damage is essential, along with the provision of medicine, food, water, shelter, and essential infrastructure. [PERSON] [32] used fine-grained topics in social media to assess disaster losses, while [PERSON] [33] proposed a novel method to classify and quantify typhoon-related damage using social media text. [PERSON]'s approach employed a multi-instance, multi-label classifier built on the BERT model to identify damage categories in social media content and quantify damage severity by measuring word similarity. [PERSON] [34] implemented a supervised learning approach using a backpropagation neural network for typhoon damage assessment. Typhoon disasters unfold as dynamic processes, with the central location of the typhoon continuously shifting and its path extending over time. The categories of disaster damage and the severity experienced by affected areas also evolve temporally. Therefore, analyzing the temporal and spatial aspects of social media data is crucial for capturing the spatiotemporal dynamics of typhoon progression. Almost all social media data are timestamped, yet posts containing geotagged locations account for less than 1% of all social media posts [35]. Early studies often plotted the locations of disaster-related posts as static points on maps and used kernel density estimation (KDE) to generate heat maps [36], where the concentration of points is represented as a raster image. [PERSON] [37] identified affected areas through spatial clustering. [PERSON] [38] applied a Latent Dirichlet Allocation (LDA) topic model and Support Vector Machine (SVM) to classify Weibo posts, proposing a weighted model based on check-in user activity to address the spatial heterogeneity of social media users, with results showing that a series of maps could reflect the spatiotemporal trends of typhoon disasters. [PERSON] [39] utilized the Stanford Named Entity Recognizer to identify people, organizations, and locations. [PERSON] and [PERSON] [40] incorporated time as an additional dimension in three-dimensional density estimation, visualizing the data through a spatiotemporal cube. In densely populated urban areas, the performance of remote sensing in flood detection can be limited; therefore, geotagged flood-related social media posts can complement remote sensing flood detection. Volunteered Geographic Information (VGI) locations have been used to generate flood probability maps through kernel smoothing applications.