In emergencies and crisis situations, the necessary response to a sudden, alarming request can be very time-critical, and the quality of the response can often determine life and death, as well as the preservation of critical infrastructure. In such extreme situations, rapid and efficient operational planning is crucial. The availability of information about resources, time requirements, and location significantly influences the effectiveness of rescue efforts. Emergency situations with time-critical response times typically arise in numerous areas, such as disaster relief operations (floods, avalanches, etc.), traffic accident operations (vehicles, trucks, trains, etc.), firefighting, medical emergencies, etc.
A crucial factor here is the earliest possible availability of up-to-date and precise spatial and action-supporting information, in the form of realistic 3D data, which includes both landscape features and structural features—even within buildings.
Information on relevant operational areas is often insufficient, 3D mapping is not strategically optimized, or is not available in a timely manner to support operations. However, if high-quality maps can be made available early and in sufficient quantities, this would represent a decisive advance in the effectiveness and efficiency of rescue operations and thus contribute significantly to improving the recovery and care of people seeking assistance.
The goal of the research project is to investigate key functionalities for the fastest possible teleoperative digital operational planning. This should provide operations management with a timely 3D representation of the operational scenario. Real-time data transmission via 5G enables the instantaneous transmission of environmental data captured by mobile mapping systems (drones, portable scanners).
Operations management can access the generated "digital twins" in a virtual environment ("remote"). These digital twins are supplemented with mission-relevant metadata generated using AI-based, automated annotations, thus supporting sound decision-making processes.
The central research focus is on scanning the environment and the structural conditions of the operational scenario as quickly as possible. The challenges include:
- the fastest possible, real-time 3D capture of environmental information using mobile mapping systems (drones, portable scanners),
- real-time transmission of the scanned environmental data via 5G parallel to the recording in order to make it available to the operational command center as quickly as possible,
- AI-supported preprocessing through automated capture of semantic information from the 3D data, including 3D object detection (pattern recognition),
so that the operational command center can make decisions faster and more efficiently with appropriate decision support.