ResQTrack: AI-Driven Crisis Detector and Geo-Mapping

Abstract

Disaster monitoring plays a critical role in reducing the impact of natural disasters and man-made disasters, but we know how the traditional methods work. Though the information is significant, it gets delayed after the disaster has occurred. This doesn’t save lives and earnings. In contrast with that, we know that on social media, the information is shared faster than news channels and other government official websites; However, the information is unstructured, inconsistent, and difficult to analyse directly. The ResQTrack system is developed to address the challenge by integrating real-time data from the social media platform X (Twitter) in addition to the help of APIs such as USGS and GDACS, processing it to identify relevant events, and extracting location details even when precise coordinates are not available. The data is processed and analysed, and then it is displayed through an interactive dashboard and on a global map. Our system also provides analytical insights and severity distribution of the regions, and it also shows the graphical representation of the data and disaster frequency trends. ResQTrack converts the raw data into structured and meaningful analytics. ResQTrack improves situational awareness and supports faster decision-making. The system demonstrates how real-time data and simple analytical techniques can be effectively used.