An Automated Mapping Approach of Emergency Events and Locations Based on Object Detection and Social Networks

The high prevalence of cellphones and social networking platforms such as Snapchat are obviously dissolving traditional barriers between information providers and end-users. It is certainly relevant in emergency events, as individuals on the site produce and exchange real-time information about the event. However, notwithstanding their demonstrated significance, obtaining event-related information from real-time streams of vast numbers of snaps is a significant challenge. To address this gap, this paper proposes an automated mapping approach of emergency events and locations based on object detection and social networks. Furthermore, employing object detection methods on social networks to detect emergency events will construct reliable, flexible and speedy approach by utilizing the Snapchat hotspot map as a reliable source to discover the exact location of emergency events. Moreover, the proposed approach aims to yields high accuracy by employing the state of the arts object detectors to achieve the objectives of this paper. Furthermore, this paper evaluates the performance of four object detection baseline models and the proposed ensemble approach to detect emergency events. Results show that the proposed approach achieved a very high accuracy of 96% for flood dataset and 94% for fire dataset.

@article{Alfalqi2025,
  title     = {An Automated Mapping Approach of Emergency Events and Locations Based on Object Detection and Social Networks},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {2},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Khalid Alfalqi and Martine Bellaiche},
  doi       = {10.14569/IJACSA.2025.0160203},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160203}
}