Calibrate data collection equipment.

“Calibrate data collection equipment.” is a supplemental task performed by Remote Sensing Technicians. Among the occupation's 22 rated tasks, workers place it 14th by importance (#9 most important). About 40% of workers say it is relevant to their job.

This is a single occupation-specific task statement from O*NET. The figures below describe how central the task is to the job and what independent studies measure about AI and this kind of work — not a prediction that the task will be automated.

Work activities this task rolls up to

O*NET groups concrete tasks into broader work activities shared across many occupations.

• Adjust equipment to ensure adequate performance. in Handling and Moving Objects

AI exposure

The OpenAI / Eloundou “GPTs are GPTs” study rates this task E2. Exposure with tools — software built on top of a language model (not the model alone) could cut the time by at least half.

Exposure measures whether a model could meaningfully speed the task up — it is an estimate of overlap with model capabilities, not a measure of whether the work will be done by software. The study's intermediate score (β) for this task is 0.50. Automation potential label: T1.

How AI is actually used on this kind of task

The Anthropic Economic Index observes how people actually use AI on tasks like this one across millions of real conversations.

• 0.009% share of AI-use records mapped to this task
• 51% of that use is work-related
• Average autonomy of the AI: 3.4 (1–5; higher = more autonomous)
• 82% of interactions still needed a human in the loop

Observed AI use describes people choosing to use AI as a tool on this kind of task today. It is augmentation and assistance, not a measure of jobs replaced.

Other tasks in this occupation

• Collect geospatial data, using technologies such as aerial photography, light and radio wave detection systems, digital satellites, or thermal energy systems. · importance 4.4
• Verify integrity and accuracy of data contained in remote sensing image analysis systems. · importance 4.2
• Correct raw data for errors due to factors such as skew or atmospheric variation. · importance 4.1
• Integrate remotely sensed data with other geospatial data. · importance 4.1
• Consult with remote sensing scientists, surveyors, cartographers, or engineers to determine project needs. · importance 4.0
• Adjust remotely sensed images for optimum presentation by using software to select image displays, define image set categories, or choose processing routines. · importance 4.0
• Manipulate raw data to enhance interpretation, either on the ground or during remote sensing flights. · importance 3.9
• Merge scanned images or build photo mosaics of large areas, using image processing software. · importance 3.9
• Develop or maintain geospatial information databases. · importance 3.8
• Monitor raw data quality during collection, and make equipment corrections as necessary. · importance 3.8
• Participate in the planning or development of mapping projects. · importance 3.7
• Maintain records of survey data. · importance 3.5
• Evaluate remote sensing project requirements to determine the types of equipment or computer software necessary to meet project requirements, such as specific image types or output resolutions. · importance 3.4
• Collect verification data on the ground, using equipment such as global positioning receivers, digital cameras, or notebook computers. · importance 3.4

See all tasks on the Remote Sensing Technicians page.

Sources for this page

Every figure above traces to a named public dataset and the exact release below — not hand-written opinion. See the full methodology for what each measure does and does not mean.

• O*NET 30.3 U.S. Department of Labor / National Center for O*NET Development
• Anthropic Economic Index v4 (2026-01-15) + v2 (2025-03-27) Anthropic
• “GPTs are GPTs” (Eloundou et al.) arXiv 2303.10130 OpenAI / academic

Data compiled June 2, 2026. Figures are estimates, not advice.

@misc{singulariki-task-16995,
  title  = {Calibrate data collection equipment.},
  author = {{Singulariki}},
  year   = {2026},
  note   = {O*NET 30.3; Anthropic Economic Index v4 (2026-01-15) + v2 (2025-03-27); “GPTs are GPTs” (Eloundou et al.) arXiv 2303.10130. Accessed June 7, 2026},
  url    = {https://singulariki.com/tasks/task-16995}
}