# AI-Enhanced Disaster Management: A Modular OSINT System for Rapid Automated Reporting
## 5 Results and Discussion
### Summary of Key Insights and Implications  
In this paper, we present ODET, a modular platform that allows both research and disaster management communities to experiment with different algorithms and develop innovative solutions. Our case studies show that ODET is not only a flexible platform but also capable of generating highly accurate and structured reports that enable rapid situational assessment. ODET serves as a platform for researchers to deploy and customize AI-based algorithms to generate reports tailored to specific scenarios. We demonstrated that an appropriate algorithm based on text embedding, community clustering, and zero-shot classification can be efficiently integrated into the ODET. Our case studies on Hurricane Harvey and the 2023 Turkey earthquake show that ODET also works reliably under thechallenges of real-world data sources, such as tweets. With AlignScore values of 0.89 and 0.84, respectively, the system demonstrated its ability to extract relevant and consistent information in a dynamic environment. ODET's modular design allows for easy replacement of individual components or models, making the platform not only future-proof but also highly adaptable. This allows researchers to integrate new technologies or models and to test their own approaches without being tied to specific technologies. This flexibility means that ODET can run on standard hardware, as well as in high-performance cloud environments, making it suitable for both small and large datasets. The results presented here show that ODET is a promising platform for quickly generating situation reports that provide decision-makers with the information they need. At the same time, ODET underscores its importance as a research platform that enables the development and testing of innovative AI-based algorithms in a controlled environment. Future work should focus on further improving ODET, particularly on how to deal with misinformation and integrate new approaches to increase the accuracy of reports. Another well-known unsolved problem is hallucinations, which we are currently trying to minimize in the reports generated using a multi-step clustering and labeling approach as well as RAG and knowledge graphs, as these technologies provide a stronger link between content and underlying data. However, hallucinations cannot be eliminated at this time because they are a known unsolved problem in AI-based text generation. Further research is needed to develop more effective mechanisms for detecting and avoiding hallucinations. This is crucial to meet the growing demands of disaster management systems and to realize the full potential of ODET as a flexible and scalable platform. Conceptualization, K.S. ; methodology, K.S. ; validation, K.S., K.B. and M.H. ; writing--original draft, K.S. ; writing--review and editing, K.B., D.A.A. and M.H. ; supervision, D.A.A. and M.H. All authors have read and agreed to the published version of the manuscript. This research received external funding from Universidad de Granada and the European Union's Horizon 2020 research and innovation programme under grant agreement No [823759] called REMESH. Not applicable. Publicly available datasets were analyzed in this study. These data can be found here: [[https://www.kaggle.com/datasets/gpreda/turkey-earthquake-tweets](https://www.kaggle.com/datasets/gpreda/turkey-earthquake-tweets)]([https://www.kaggle.com/datasets/gpreda/turkey-earthquake-tweets](https://www.kaggle.com/datasets/gpreda/turkey-earthquake-tweets)) and here: [[https://www.kaggle.com/datasets/dan195/hurricaneharvey](https://www.kaggle.com/datasets/dan195/hurricaneharvey)]([https://www.kaggle.com/datasets/dan195/hurricaneharvey](https://www.kaggle.com/datasets/dan195/hurricaneharvey)) (accessed on 30 June 2024). The authors declare no conflicts of interest. The following abbreviations are used in this manuscript: