Surveying and Mapping Technicians

Perform surveying and mapping duties, usually under the direction of an engineer, surveyor, cartographer, or photogrammetrist, to obtain data used for construction, mapmaking, boundary location, mining, or other purposes. May calculate mapmaking information and create maps from source data, such as surveying notes, aerial photography, satellite data, or other maps to show topographical features, political boundaries, and other features. May verify accuracy and completeness of maps.

The occupation "Surveying and Mapping Technicians" has an automation risk of 54.3%, which closely reflects its base risk of 55.0%. This moderate risk arises from the heavy reliance on routine, repetitive tasks that can be codified and performed by machines or software. For instance, tasks such as positioning and holding vertical rods or targets for theodolite operators, checking all map layers for accuracy, and designing or developing geographic information databases are highly automatable. These duties involve precision, data processing, and physical activities that can be readily executed by advanced robotics or mapping software, especially as technologies like geographic information systems (GIS) and autonomous survey drones continue to mature. Despite these risks, certain aspects of the job remain resistant to automation. Tasks like supervising or coordinating teams, setting out and recovering survey markers, and searching for section corners, property irons, or survey points require human judgment, adaptability, and situational awareness. These activities often involve navigating unpredictable environments, interpreting ambiguous information, and making real-time decisions—skills that are currently difficult for AI systems to replicate. Additionally, effective supervision and coordination entail interpersonal communication, leadership, and practical problem-solving abilities that go beyond rote mechanization. Crucially, bottleneck skills such as originality, assessed at 2.8% and 2.5%, present significant hurdles for full automation in this field. Originality reflects the need for creative problem-solving, adapting to novel situations, and generating new solutions—capabilities that AI still struggles to emulate. While machines excel at repetitive calculations and data management, designing unique approaches to field survey challenges or correcting unforeseen map errors demands a level of ingenuity that resists standardization. As long as originality remains a core requirement, complete automation of surveying and mapping technician roles will likely be constrained, keeping human involvement crucial for safe and efficient operations.