## 1 Introduction
[PERSON] [13] developed a standardized social media text dataset for disaster relief, reannotating existing disaster image datasets to address four tasks: disaster type detection, identifying the presence of disaster information, humanitarian aid, and damage assessment. [PERSON] [14] used a spatial-logistic growth model based on social media data to rapidly estimate earthquake-affected areas. Disaster information on social media can also be highly personalized, helping individuals receive assistance. During disasters, people can share their emotions and experiences via social media platforms. [PERSON] [15] extracted fine-grained public sentiment information from social media big data and applied it to disaster mitigation services. [PERSON] [7] identified eight common tasks related to the extraction and analysis of VGI (Volunteered Geographic Information) from social media data concerning natural disasters, including (1) extraction of disaster-related social media posts, (2) humanitarian aid classification, (3) witness post identification, (4) sentiment analysis, (5) damage and severity assessment, (6) flood level estimation, (7) disaster phase classification, and (8) unsupervised topic modeling. During disasters, the multimodal content on social media becomes a critical channel for disseminating key information. Although previous studies have primarily focused on topic modeling and sentiment analysis of text data to understand public needs and emotions and to allocate rescue resources accordingly, image data also provide a rich reflection of the disaster situation. Traditional methods, such as keyword filtering, have limitations [16], while natural language processing (NLP) techniques, including models like TF-IDF [17] and Word2 Vec [18], have significantly improved the classification of disaster-related texts. Further research has explored the application of deep learning in short text classification, such as using TextCNN [19] and LSTM [10] networks, as well as unsupervised learning methods like LDA for tweet classification. [PERSON] [20] applied a probabilistic classification model with n-gram features to identify genuine power outage tweets and used LSTM to extract outage locations from the text. [PERSON]'s comparative study of Word2 Vec, GloVe, ELMo, and BERT showed that, possibly due to the informal structure and character limitations of tweets, the performance of BERT and ELMo was very similar to that of Word2 Vec and GloVe in disaster tweet classification [21]. Simultaneously, image analysis demonstrates great potential in disaster response, though its application in social media data analysis remains relatively uncommon. Advances in computer vision techniques, from SURF visual features [22] to deep convolutional neural networks (DCNNs) [23], have provided new avenues for the automatic identification of disaster-related images. Research suggests that multimodal deep learning frameworks that combine image and text features can enhance the performance of image data classification [24], which is critical for the rapid and accurate assessment of disaster impacts and response efforts. Therefore, leveraging both text and image information from social media holds immense potential for disaster response and damage assessment. [PERSON]'s study [25] found that using text features improved classification performance when classifying corresponding images. [PERSON] [26] confirmed the significant advantages of multimodal data in enhancing the accuracy of spatial information extraction. Acquiring an appropriate number of labeled data after a disaster can delay situation awareness tasks. Therefore, in this study, we employed transfer learning to apply models trained on past disaster events and datasets for situational awareness of Typhoon \"Haikui\". In recent years, the rapid development of deep learning techniques in the fields of computer vision and natural language processing has enabled the extraction of more disaster-related information from social media, such as disaster conditions and affected locations.