Emergencies are dynamic environments – maintaining situational awareness during them isn’t easy. That’s where digital tools with integrated elements of artificial intelligence (AI) and machine learning (ML) can help.
But how, exactly? In the following, we delve into five impactful applications of AI in emergency response management.
The growing role of AI in emergency response management
No doubt, AI has dominated the tech conversation for the last 12 to 18 months, thanks to rapid developments in large language model (LLM) platforms, chatbots, and other AI-driven tools. These innovations are increasingly being adapted for critical applications in emergency response management.
From modeling potential outcomes to real-time monitoring, AI and ML are helping response teams build and maintain situational awareness. These tools analyze continuously evolving data to keep incident commanders informed with accurate, dynamic updates.
Of course, AI-driven technologies in emergency response management don’t operate autonomously unless enabled. For emergency management, these tools are designed with a human-in-the-loop approach. But while AI-enabled tools for emergency management aren’t autonomous, they can perform tasks for which humans were previously responsible on an automated basis.
What tasks?
Real-world applications of AI in emergency response
Well, as AI technology is continually improving, a breakthrough can propel rigorously tested and ready-to-go functionality into the emergency response space at any moment. Five very specific applications of this technology include:
1. Crowd and traffic management
Vehicles within an emergency environment can present serious challenges. Depending on the type of emergency, some roadways may be unexpectedly obstructed or completely blocked, leaving fewer avenues for pedestrian vehicles to clear the area and emergency vehicles to enter. Most importantly, the people within those vehicles must get to safety as fast as possible – but without creating a potential choke point or jamming the roadways altogether.
When granted access to cameras, AI-enabled emergency response software can help assess crowd size and crowd density and recommend safe pathways for crowd dispersal to avoid creating crush situations, leading to safer crowd dynamics on the ground as the area is cleared.
Similarly, AI analysis can assess which roadways are no longer safe or available for use and recommend the ones teams can use to guide pedestrian vehicles away safely.
2. Digital mapping of low-visibility environments
Emergencies often occur in conditions with limited visibility, such as smoke, fog, or structural damage that obscures navigation.
For situations like these, AI-enabled emergency response software can assess floor plan data from a combination of sources, such as CAD files, PDF documents, and photographs, quickly generating a three-dimensional interactive indoor map accessible on an array of smart devices. Such models help emergency response teams identify safe entrances and exits, giving them vital spatial intelligence to move into dangerous environments.
3. Real-time language detection and translation
In an emergency setting, not all witnesses or affected individuals will speak the same language as response teams.
Thankfully, AI-enabled platforms can offer real-time language detection and translation services that are scalable across multiple devices. That way different team members can take accounts from witnesses who speak different languages.
Gaining access to valuable information about an emergency situation by enabling witnesses to speak in their native languages serves to improve information quality, helping response teams make better decisions.
4. Video analysis for faster response
Given the sheer volume of available footage from urban surveillance cameras, it can be hard for emergency response teams to parse vital information in the course of a crisis.
This is where the analytics capability of AI-enabled emergency response software can prove its value. The AI-powered elements of these tools can continuously perform algorithmic analysis across an entire network of cameras at once, detecting individuals, identifying key factors, isolating precise locations, and recommending the dispatch of emergency services as they’re needed, helping response teams to find and treat people in need of assistance more quickly.
5. Automated reports and documentation
Emergency response teams are trained to frequently compile available data into detailed reports that inform the common operating picture (COP), upon which an incident commander or the unified command of an Emergency Operations Center (EOC) relies for situational awareness.
Emergency response management software with AI and ML elements can quickly and accurately assess troves of data from multiple sources, synthesize them with running news feeds and other information streams. They can also provide a clear, accurate synopsis as events unfold. This way, incident commanders can share a unified COP with all team members while the team continuously meets all standards for transparency and accountability.
Finally, by all accounts, the future of emergency response is smart, data-driven, and AI-enhanced. And as AI tools continue to evolve, we expect to see increased integration into emergency management workflows, which will offer even more opportunities to save lives, speed up response, and improve decision-making during crises.
Of course, emergency response isn’t the only aspect of resilience that can be enhance by AI tools. AI also has a clear role to play in business continuity plans. What role, exactly? Check out our article on The Role of AI in Business Continuity Planning to learn more.
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