# AI-Enhanced Disaster Management: A Modular OSINT System for Rapid Automated Reporting
## 1 Introduction  
Natural and man-made disasters pose increasingly complex challenges to disaster management. With the increasing frequency and intensity of such events, rapid and accurate information gathering is critical for timely decision-making and action [1; 2]. In this context, microblogging platforms such as Twitter/X have proven to be valuable sources of real-time information, often reacting to unfolding situations faster than traditional news sources by broadcasting eyewitness observations [3; 4; 5; 6]. However, these platforms present challenges [4; 5]. Large amounts of unstructured data must be processed, and relevant information must be filtered from a multitude of irrelevant or erroneous posts [7; 8].  
Automated systems using artificial intelligence (AI) offer a promising approach to accelerate and optimize the analysis of such data volumes. In particular, techniques such as text embedding, community-based clustering, and zero-shot classification make it possible to identify semantically relevant content and group events and present them in a structured way without the need for models that have been specifically trained for the respective event [7; 8]. These techniques have the potential to form the basis for efficient and rapid reporting of disasters.  
This paper presents the Open-Source Intelligence Disaster Event Tracker (ODET), a modular platform that provides customizable endpoints and agents for each processing step. ODET enables the implementation of AI-enhanced algorithms to respond to various complex disaster scenarios. To evaluate ODET, we conducted two case studies using unmodified AI models to demonstrate its base performance and potential applications. Through our case studies on Hurricane Harvey and the 2023 Turkey earthquake, we show how complex tasks can be quickly broken down with ODET while achievinga score of up to 89% using the AlignScore metric [9]. ODET enables compliance with Berkeley protocol requirements [10] by ensuring data privacy and using privacy-preserving processing methods.  
Disaster management includes prevention, preparedness, response, and recovery, which together form the disaster management cycle [11]. ODET can be used in the response and recovery phases to provide quick and accurate situational reports. In addition, ODET offers potential for the training phase by providing realistic scenarios and data-driven analysis. This not only improves the preparation and training processes but can also be used to simulate real disasters and fine-tune models.  
This paper contributes to the advancement of OSINT methods in the field of disaster management by introducing ODET, a modular platform that can be used to enable faster situational awareness in disaster scenarios.  
The remainder of this paper is organized as follows: In Section 2, the current state of research on open-source intelligence (OSINT) and artificial intelligence in disaster management is discussed in detail. Section 3 details the materials and methods used. Section 4 discusses the execution of case studies of Hurricane Harvey and the 2023 Turkey earthquake using ODET. Finally, in Section 5, we present and discuss the main results.