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

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.