Airborne near-real-time monitoring of assembly and parking areas in case of large-scale public events and natural disasters

Abstract

A critical requirement for an effective and coordinated response by public entities tasked with management, security, and relief during large-scale public events or natural disasters is the availability of current situational information. However, today there is a lack of comprehensive operational systems allowing a near-real-time (NRT) collection, visualization, and provision of situational information for larger areas. In this study a methodological framework is proposed, which allows an NRT extraction and visualization of situational information based on aerial image acquisition. The framework combines digital image analysis using a generic supervised information extraction approach based on statistical modeling with a downstream web-based visualization component realized through an automatic update of web services. Even though being applicable for different scenarios, the workflow will be demonstrated for the specific use-case of a NRT monitoring of open spaces including assembly and parking areas. Compared to other approaches, image analysis results indicate a high robustness and a low demand for computational power sources (7 seconds per image). Due to a high degree of automation, the proposed workflow contributes to a NRT ‘end-to-end’ monitoring system, which was developed within the VABENE (German acronym for ‘traffic management under large-scale public events and disaster conditions’) project covering all parts from the acquisition of raw aerial imagery to the dissemination of information products to end-users.

Keywords:

• supervised classification
• emergency response
• web-service
• rich internet applications
• decision support

Acknowledgments

The research presented in this article builds upon the DLR-funded traffic project VABENE. We would like to acknowledge and thank the whole project team for the close interdisciplinary cooperation, and giving us the opportunity to integrate our workflow into the main monitoring system. Special thanks go also to Mathias Böck, Rolf Schleicher, and Amelie Stolle for providing technical assistance regarding web-client development and accuracy assessments.