AI-Powered Target Location: Finding the Place Responders Actually Need to Go
By Tracy “Mac” McElvaney, Chief Product & Innovation Officer, GeoComm
Public safety has made significant progress in improving emergency caller location, but locating the caller is not always the same as identifying where responders actually need to go. In many incidents, those two places are different. A caller may be outside a building while the emergency is several floors above them. A large school, hospital, airport, or campus may have a single street address but dozens of possible entrances, rooms, wings, gates, or response points. In those environments, getting responders to the correct property is only the beginning.
The next evolution of location intelligence may be the ability to identify the operational target of the response. This means understanding not only where the incident is generally located, but which specific entrance, floor, room, access point, person, vehicle, or hazard responders actually need to reach. Artificial intelligence could play an important role in helping public safety systems interpret that context more quickly.
From Caller Location to Operational Target
Emergency communications systems have historically focused on determining where a 9-1-1 call originated. That remains essential, but modern response environments increasingly require more detailed context. A street address may get responders to the right parcel yet still leave them without enough information to determine the most effective point of access.
This challenge becomes particularly clear in large or complex facilities. Hospitals may have separate emergency, staff, public, and service entrances. Schools may contain multiple buildings and secured access points. Industrial facilities may span large areas with internal roadways and restricted zones. Even a geographically accurate caller location may not tell responders where the emergency is occurring or how best to reach it.
Target location expands the location problem from “Where is the caller?” to “Where does the response need to occur?”
An incident may have several relevant response locations: where the emergency is occurring, where personnel should enter, and where equipment should stage. Those locations may differ by responding resources. An ambulance, fire crew, and drone may need different approaches or handoff points while responding to the same incident.
How AI Could Help Connect the Context
AI has the potential to help connect multiple sources of information that today may be reviewed independently. Caller location, CAD information, GIS data, indoor maps, imagery, sensors, drone feeds, and other operational data can each provide part of the incident picture. The challenge is assembling those pieces quickly enough to be useful.
An AI-supported workflow could potentially analyze those sources together and surface the most relevant location context for a telecommunicator or responder. For example,
A caller might report, “Smoke is coming from the second-floor science lab near the east stairwell.” An AI-supported system could compare that description with authoritative building, floor, room, and entrance information to suggest a location for review. If several rooms match, it should surface the ambiguity and help the telecommunicator identify what clarification is needed.
The objective is not to replace human judgment. It is to reduce the amount of manual interpretation required to reach an informed decision.
Location Intelligence Has to Include Meaning
Coordinates alone do not always provide enough context for public safety. A latitude and longitude, and vertical location reference can identify a point, but emergency response often requires understanding what that point represents.
A system may need to determine whether the location is inside or outside a structure, which building or floor is involved, which entrance provides the best access, or whether a road or doorway is temporarily unavailable. As more information becomes available from indoor maps, sensors, connected devices, and drones, public safety systems will increasingly need to interpret the relationship between those data sources.
This is where location intelligence and AI become closely connected. AI can help identify patterns and relationships across multiple data sources, while authoritative geospatial information provides the context needed to understand where those observations exist in the real world.
Target Location Becomes More Important as Response Technology Evolves
The need for more precise target location will also increase as public safety begins using a wider range of response technologies. Drones, robotics, connected vehicles, and other automated or semi-automated systems may require more precise geographic instructions than traditional response resources.
A person may be able to hear “respond to the north side of the building” and use experience to interpret what that means. A drone or robotic system may require a specific coordinate, altitude, access route, or floor designation. As these technologies become more common, the quality and precision of the underlying location information will become increasingly important.
This does not mean that every incident will require highly detailed spatial intelligence. It does mean that public safety systems should be capable of providing greater precision when the situation demands it.
Better Technology Should Reduce Complexity
One of the risks associated with emerging technology is that every new capability creates another interface, another data feed, or another source of information that public safety professionals have to manage. That is not necessarily progress.
The real value of AI-powered target location would come from reducing complexity. Instead of requiring a telecommunicator to search multiple systems, the technology should help bring the most relevant information together. Instead of giving responders several conflicting views of the incident, it should help organize location context into something operationally useful.
The measure of success should not be how much information the system can display. It should be whether the technology helps public safety professionals understand where the problem is and what they need to do next.
Evaluation should measure correct target identification, time to confirm the location, wrong-entrance arrivals, and the effort required from telecommunicators.
The location’s meaning should travel with it. When information moves from call handling to CAD, mobile applications, or drone systems, the building, floor, room, access point, and verification status should remain connected. Shared authoritative spatial context, delivered through interoperable services, could help each participant work from a consistent understanding as the incident evolves.
Trust Remains the Foundation
AI-supported location intelligence will still depend on the quality of the data behind it. A system needs to know whether an entrance is current, whether a building map is authoritative, whether a road closure is temporary, and whether a location was verified or inferred.
That makes governance, data quality, and source authority just as important as the AI itself. A recommendation can appear highly precise while still being based on incomplete or outdated information. In public safety, precision without trust can create additional risk.
Human oversight will remain essential, particularly when AI is combining information from multiple sources or making inferences about the location of an incident.
Each suggested target should show its supporting sources, their freshness, and whether the location was verified or inferred. Conflicting evidence should remain visible, and the system should indicate when information is insufficient to recommend a specific location. Telecommunicators should be able to confirm, correct, or override the suggestion, with that decision carried forward as new information arrives.
The Next Step in Identifying Emergency Location
Public safety will continue improving its ability to locate callers, but the next challenge is broader. The industry will increasingly need to understand where the response itself needs to focus.
AI-powered target location represents one possible step in that direction. By connecting caller location with GIS, indoor mapping, sensors, imagery, drones, and other operational data, future systems may help public safety professionals identify the location that matters most to the incident.
The goal is not simply greater precision. It is better operational understanding.
If technology can help reduce uncertainty and get the right resource to the right place more quickly, then location intelligence becomes more than a point on a map. It becomes part of the decision-making process.
–> Talk with a GeoComm expert to learn more about using AI-powered target location and getting responders to the right location fast.
