# AI-Enhanced Disaster Management: A Modular OSINT System for Rapid Automated Reporting
## 3 Materials and Methods
### Odet  
The Open-Source Intelligence Disaster Event Tracker (ODET) is based on a modular architecture that allows for flexible adaptation to different scenarios. The system is divided into endpoints and agents. ODET supports specific endpoints, such as API endpoints for connecting external data sources, classification endpoints for filtering irrelevant or inappropriate content, text inference endpoints for semantic analysis, and embedding endpoints for creating textual representations. ODET also provides defined agent types: the API agent for data collection, the classification agent for categorization and filtering, the embedding agent for semantically grouping content, the knowledge graph agent for extracting relationships, and the summary agent for generating detailed reports. These agent types can be flexibly combined and linked in the processing pipeline to satisfy specific requirements. The modular design makes it easy to replace or extend individual components, keeping ODET future-proof and compatible with new technologies or models.  
In the case studies, unmodified AI models were used to show that ODET is powerful, even without additional training steps. The GIST-all-MiniLM-L6-v2 model was used for sentence embedding to generate high-dimensional text representations and capture semantic relationships between the analyzed data. The quantized model Mistral-Nemo-Instruct-2407-Q6_K_L was used for zero-shot classification and other agents, such as the knowledge graph agent and the summary agent. This model is not a pre-trained classifier but a basic Instruct model that is dynamically adapted by ODET using GGBNF grammar and specially defined prompts to generate zero- or multi-shot classifiers and other specialized outputs. The context window size was 65,536 tokens (n_ctx = 8192 \(\times\) 8) with a batch size of 512 tokens (n_batch = 512), and 10 GPU layers (n_gpu_layers = 10) were used. For hate-speech detection, the KoalaAI TextModeration model was used, which analyzes content with a batch size of 64 tokens (batch_size = 64) while considering truncations (truncation = True).  
Computing requirements vary depending on the ODET deployment scenario. In the case studies, the system successfully ran on a MacBook Air with an Apple Silicon M3 processor and 24 GB of shared RAM, although this configuration was not optimal. Using the Mistral-Nemo-Instruct-2407-Q6_K_L quantized model with a context window of up to 65,536 tokens and a batch size of 512 tokens typically requires at least 10 GB of RAM for model inference alone. Additional memory is required to process the input data and to perform parallel tasks, such as creating embeddings or performing classifications, bringing the total memory requirement to approximately 16-20 GB of RAM. On the CPU side, at least 8 cores are recommended to efficiently support multi-threaded processing. Running on an A100 GPU in the Google cloud proved to be much more powerful, processing the entire pipeline at a much higher speed and with lower latency.  
The ability to run ODET on both consumer hardware and cloud-based endpoints renders the system highly scalable and flexible. This scalability not only allows the system to adapt to varying resource availability but also provides the basis for rapid deployment in real-world disaster situations where time and availability are critical.