# AI-Enhanced Disaster Management: A Modular OSINT System for Rapid Automated Reporting
## 3 Materials and Methods
### Zero-Shot Classification and Information Extraction  
For the case studies presented, zero-shot classification was realized using a 6-bit quantized version of the Mistral-Nemo-Instruct-2407 model that was specifically optimized for instructional tasks by its creators [49]. This model is a fine-tuned version of Mistral-Nemo-Base-2407, which was jointly developed by Mistral AI and NVIDIA. It is designed to ensure both high accuracy and efficiency and can be easily replaced by more advanced models in future studies. We used this model in a quantized but unmodified version. The quantized version of this model was specifically chosen for efficient use of memory and computing power. The model required approximately 10 GB of main memory with a near-perfect quality, making it particularly suitable for use in a wide range of hardware configurations. A key aspect of ODET is its ability to freely exchange models and adapt them to specific requirements. The goal was to develop a system that does not rely on specially trained models, and can, therefore, be flexibly applied to different future disasters. This architecture allows for the exchange of AI models, embedding models, and classifiers to ensure that researchers can benefit from new developments or integrate their own models without being tied to specific technologies. Another central component is the ability to customize the prompts and grammar used to control the model output. The flexibility of the system allows researchers to define specific output requirements realized through JSON schema-based grammar. The schema used for the case studies consisted of disaster-event-related, location-related, and headline-related components. These components ensure that the model's output always includes information on whether a cluster is related to an event of interest, where the event is occurring, and which headline is the most appropriate. Using this structured grammar, we can ensure that the results are consistent and analyzable. The flexibility of this methodology allows us to define different questions and output formats for different use cases. A specially developed prompt template that builds upon the grammar utilized earlier was used for the classification. As an example, the template for the Hurricane Harvey case study contains specific instructions that prompt the model to provide concise answers to questions such as, \"Does the tweet refer to a hurricane?\", \"Where is the event located?\"\"What would be an appropriate headline?\". This structured approach can be changed to specifics regarding the event of interest and ensures that the classified data are consistent and easy to analyze. The model was dynamically generated and adapted to a specific use case to provide accurate and meaningful results. During the analysis, all clusters were classified thematically. Clusters that were classified as irrelevant were discarded to ensure the quality and meaningfulness of the final situation report. The cluster formation and processing method helps to increase the efficiency of data processing by processing large numbers of tweets in a shorter time and with fewer computational resources. The results of this process were stored in Python dictionaries and were subsequently used for further analysis. Data were filtered based on the output JSON values, excluding records classified as irrelevant. Occasional misclassifications do not have a significant impact on the overall context of the data analysis. The clustering process in ODET consists of two distinct phases. First, the vector embeddings of the tweets are generated using a sentence embedding model that transforms the data into a high-dimensional vector space in which semantic relationships are preserved. These embeddings are then sorted and clustered using a cosine similarity threshold such that only semantically relevant tweets are part of the same cluster. Choosing an appropriate threshold for cosine similarity is a trivial procedure performed empirically by the user.