## 4 Conclusions
### Limitations and Future Research  
Although this study proposes an effective framework for disaster situation awareness, several limitations remain. First, incomplete geographic coverage and network interruptions may lead to gaps in social perception information. Second, the limited spatial resolution of some images on social media may result in insufficient segmentation accuracy, affecting the precision of disaster damage classification. Furthermore, the diversity and inconsistency of textual descriptions could cause errors in disaster classification by natural language processing models. Lastly, while the geographical detector can analyze spatial heterogeneity, it is constrained by the number of input variables, potentially limiting its ability to uncover deeper spatiotemporal patterns.  
To address these limitations, we propose the following improvements for future research: (1) Multi-source Data Integration: Supplement social media data by leveraging satellite remote sensing imagery, drone footage, or surveillance videos to obtain disaster information from network-disconnected areas. (2) Utilization of Indirect Information: Recognize that family members of stranded or missing individuals may share relevant personal information and locations via social media, or that affected individuals might post disaster information after relocating to safe areas. These indirect data sources could be used to infer disaster situations in network-disconnected regions. (3) Model Optimization: Future efforts could focus on enhancing models by integrating network traffic monitoring and disconnection detection technologies to identify potential information gaps and prioritize these areas for analysis, thereby improving the comprehensiveness and accuracy of disaster assessment. (4) Enhancement of Training Data and Algorithms: Refine the training datasets and algorithms for models such as Deeplab v3+ and ERNIE to better handle various disaster scenarios, thereby improving their generalization ability and robustness. (5) Integration of Additional Spatial Analysis Tools: Employ advanced spatial analysis techniques, such as geographically-weighted regression, to delve deeper into the geographical factors influencing disaster progression, uncovering more complex characteristics of spatial heterogeneity.  
Conceptualization, S.G. and T.Y.; methodology, Y.X. and N.M.; software, Y.X. and X.W.; validation, Y.X. and X.W.; formal analysis, T.Y. and Y.X.; investigation, Y.X. and H.H.; resources, S.G; data curation, Y.X.; writing--original draft preparation, Y.X. and T.Y.; writing--review and editing, S.G. and N.M.; visualization, Y.X. and H.H.; supervision, T.Y. and N.M.; project administration, T.Y.; funding acquisition, S.G. and N.M. All authors have read and agreed to the published version of the manuscript.  
The second batch of \"Fourteenth Five-Year Plan\" teaching materials for general higher education in Henan Province, China: Principles and Applications of Geographic Information System (Third Edition).  
Not applicable.  
The original data presented in this study are openly available. The URLs are referred to in the main text.  
The authors declare no conflicts of interest.