Often handed to AI
Task areas most often handled directively in observed AI conversations — candidates to delegate with light review.
- Compile and format image data to increase its usefulness. · 1.5%
Remote Sensing Scientists and Technologists
Occupation · SOC 19-2099.01
Apply remote sensing principles and methods to analyze data and solve problems in areas such as natural resource management, urban planning, or homeland security. May develop new sensor systems, analytical techniques, or new applications for existing systems.
Also called: Geospatial Intelligence Analyst · Image Scientist · Remote Sensing Analyst · Remote Sensing Scientist · Research Scientist · Scientist · Sensor Specialist · All Source Intelligence Analyst · Commercial Drone Operator · Commercial Drone Pilot · Drone Operator · Drone Pilot
Job family: Life, Physical, and Social Science Occupations
Take this to your AI
A source-stamped Markdown brief of this occupation — paste it into an agent, or fetch
/roles/role-19-2099-01/context.md directly.
AI work map
A fast read on where AI already shows up in this occupation, where it stays a copilot, where humans remain in the loop, and what the labor market is doing. Built from observed Claude.ai conversations mapped to O*NET tasks and from published research — measures of usage and exposure, not advice or predictions that the job is going away.
Keep a human in the loop
Task areas where a human was still judged necessary in a large share of observed conversations — not a safety ruling, an observed-need signal.
- Compile and format image data to increase its usefulness. · 94.6% need a human
Skills it runs on
The capabilities O*NET rates most important for this occupation — the human ground the work is built on.
See all skills →Market signal
Independent published positions, read together — not a forecast.
- About average employment outlook (+0.6% by 2034)
- 2,000 openings/yr
- Moderate AI exposure
- Observed AI use leans automation
- Median pay $117,960/yr
- Can largely be done remotely (teleworkable)
- A reversal from 2013 — rated low automation risk then (45th), high AI task-overlap now (74th)
74th-percentile task overlap — yet about 2,000 openings a year (+0.6% projected, BLS), and observed AI use leans 2770% copilot, not hand-off (AEI) . What exposure means →
AI & job outlook
What today's research says about this occupation's exposure to AI, how AI is actually being used in it, and where employment is headed. These are positions within published studies — measures of exposure and usage, not predictions that this job will disappear.
Exposure to current AI
Each study uses its own scale, so the raw scores are not comparable across rows — the percentile (this job's rank among all U.S. occupations with data) is the comparable figure, and sizes the bars.
| Measure | Rank vs all occupations | Percentile | Score |
|---|---|---|---|
| Overall AI exposure (Felten et al.) High | 80th | 1.1 | |
| LLM task exposure, γ (OpenAI / Eloundou) High | 80th | 0.9 | |
| AI assistant applicability (Microsoft) Moderate | 59th | 0.2 |
OpenAI's exposure study scores tasks three ways: with a language model alone (α 0.0), with simple added tooling (β 0.5), and including AI-powered software (γ 0.9). Higher means more of the job's tasks could be done at least twice as fast — not that they will be automated away.
Most of this job's tasks can be done remotely (Dingel–Neiman), which tends to track with higher digital and AI exposure.
Historical automation estimate (2013)
A pre-LLM (2013) estimate of how automatable this job is by computerization and robotics. Shown for historical context only — it is not part of any current AI ranking.
Frey–Osborne probability 0.4 · 45th percentile among occupations · Moderate
How AI is actually used in this job
Among measured AI assistant conversations mapped to this occupation (Anthropic Economic Index, 2026-01-15), these task types came up most. These are shares of observed AI conversations — not shares of the job, of worker time, or of what could be automated.
| Compile and format image data to increase its usefulness. | 1.6% | |
| Prepare or deliver reports or presentations of geospatial project information. | 0.2% |
Job outlook
Independent U.S. Bureau of Labor Statistics employment projection for 2024–2034 — a labor-market forecast, not an AI-impact forecast.
| Outlook | About average · +0.6% by 2034 |
| Projected annual openings | 2,000 |
| Employment 2024 → 2034 | 31,900 → 32,100 |
“Annual openings” counts new jobs plus replacements for workers who leave the occupation, so it can be large even when growth is modest.
Where this work sits on the global GenAI gradient
The ILO's 2025 global study scores generative-AI exposure on the international ISCO-08 occupation system, not US SOC. Bridged through the published (and approximate, many-to-many) IBS O*NET-SOC ↔ ISCO-08 crosswalk, this US occupation corresponds to the international 4 occupations below. Exposure here means how much of the work's tasks today's AI can attempt — task overlap, not automation, adoption, or jobs lost.
Remote Sensing Scientists and Technologists sits at the 78th percentile of 427
occupations on the global GenAI task-exposure gradient
— exposure rose from 2023 to 2025. Each dot is one occupation; the
ringed one is this work. Exposure is task overlap, not automation or jobs lost.
41% mean task exposure (2025)
78th percentile of 427 placed occupations
+5 pts shift 2023 → 2025
| International occupation (ISCO-08) | Task exposure (2025) | Most tasks fall in |
|---|---|---|
| Meteorologists · 2112 | 54% | Gradient 3 |
| Chemists · 2113 | 39% | Minimal |
| Physicists and Astronomers · 2111 | 38% | Gradient 1 |
| Geologists and geophysicists · 2114 | 36% | Minimal |
Read the whole six-band gradient on the GenAI exposure gradient page. The crosswalk is approximate: a US occupation can map to several international ones, and the ILO scores describe the international occupation, not this exact US role.
Working with AI in this job
How people actually apply AI to this occupation's tasks, from Claude.ai (Free and Pro) conversations in the Anthropic Economic Index, 2026-01-15. This is one AI assistant's consumer sample — not all AI, not the whole workforce. Autonomy and the collaboration mix are model-rated estimates; figures below the sample floor are hidden.
| Augmentation vs. automation | 27.7% working with AI · 63.5% handed to AI |
| Most common way people use AI here | Directive · AI does it; you give the instruction |
| Typical AI autonomy | 3.0 / 5 · higher = AI acts more independently |
| Used for work (vs. personal / coursework) | 52.7% |
What people delegate to AI
The role's most common tasks in AI conversations, each tagged with how people work with the AI on it. “Usage” is the share of observed conversations, not of the job.
| Task | How | Usage |
|---|---|---|
| Compile and format image data to increase its usefulness. | Directive | 1.5% |
Where a human is still needed
Tasks where the model most often judged that a person remained necessary — a useful read on the current boundary, not a guarantee.
| Compile and format image data to increase its usefulness. | 94.6% |
What people most often hand AI here
Example prompts phrased from the tasks people most often delegate to AI in this occupation (Anthropic Economic Index). Each shows the underlying measured task and its share of observed AI use. They are suggested phrasings of real tasks — starting points, not endorsed instructions.
-
Help me compile and format image data to increase its usefulness.From: Compile and format image data to increase its usefulness. · 1.5% of measured AI use · directive
Tasks
All 24 tasks O*NET lists for this occupation, ordered by importance. Each links to its own page with AI-exposure and observed-use detail.
- Manage or analyze data obtained from remote sensing systems to obtain meaningful results. Core · importance 4.6
- Analyze data acquired from aircraft, satellites, or ground-based platforms, using statistical analysis software, image analysis software, or Geographic Information Systems (GIS). Core · importance 4.5
- Integrate other geospatial data sources into projects. Core · importance 4.4
- Organize and maintain geospatial data and associated documentation. Core · importance 4.4
- Compile and format image data to increase its usefulness. Core · importance 4.3
- Prepare or deliver reports or presentations of geospatial project information. Core · importance 4.2
- Discuss project goals, equipment requirements, or methodologies with colleagues or team members. Core · importance 4.2
- Process aerial or satellite imagery to create products such as land cover maps. Core · importance 4.1
- Design or implement strategies for collection, analysis, or display of geographic data. Core · importance 4.0
- Develop or build databases for remote sensing or related geospatial project information. Core · importance 4.0
- Collect supporting data, such as climatic or field survey data, to corroborate remote sensing data analyses. Core · importance 3.9
- Monitor quality of remote sensing data collection operations to determine if procedural or equipment changes are necessary. Core · importance 3.9
- Train technicians in the use of remote sensing technology. Core · importance 3.9
- Set up or maintain remote sensing data collection systems. Core · importance 3.8
- Direct all activity associated with implementation, operation, or enhancement of remote sensing hardware or software. Core · importance 3.7
- Attend meetings or seminars or read current literature to maintain knowledge of developments in the field of remote sensing. Core · importance 3.7
- Conduct research into the application or enhancement of remote sensing technology. Core · importance 3.7
- Recommend new remote sensing hardware or software acquisitions. Supplemental · importance 3.5
- Use remote sensing data for forest or carbon tracking activities to assess the impact of environmental change. Core · importance 3.5
- Develop automated routines to correct for the presence of image distorting artifacts, such as ground vegetation. Core · importance 3.4
- Develop new analytical techniques or sensor systems. Core · importance 3.4
- Participate in fieldwork. Core · importance 3.3
- Apply remote sensing data or techniques, such as surface water modeling or dust cloud detection, to address environmental issues. Core · importance 3.2
- Direct installation or testing of new remote sensing hardware or software. Supplemental · importance 3.1
Emerging tasks
Newer responsibilities O*NET has flagged as growing for this occupation.
- Develop protocols and procedures for planning and executing drone-based remote sensing missions to ensure they comply with standards and requirements.
Work activities
- Analyze geological or geographical data.
- Develop technical or scientific databases.
- Prepare scientific or technical reports or presentations.
- Record research or operational data.
- Compile geographic or related data.
- Develop environmental research methods.
- Direct technical activities or operations.
- Collect geographical or geological field data.
- Collect environmental data or samples.
- Create images or other visual displays.
- Evaluate new technologies or methods.
- Collaborate with others to develop or refine designs.
- Discuss design or technical features of products or services with technical personnel.
- Develop software or applications for scientific or technical use.
- Develop specifications for new products or processes.
- Develop technical specifications for systems or equipment.
- Assess equipment functioning.
- Analyze physical, survey, or geographic data.
- Review professional literature to maintain professional knowledge.
- Attend conferences or workshops to maintain professional knowledge.
- Advise others on the development or use of new technologies.
- Set up laboratory or field equipment.
- Train personnel in technical or scientific procedures.
- Apply knowledge or research findings to address environmental problems.
Knowledge, skills & abilities
O*NET importance rating, from 1 (not important) to 5 (extremely important).
Knowledge
| Geography | 4.8 | |
| Computers and Electronics | 4.2 | |
| Mathematics | 4.2 | |
| Engineering and Technology | 3.9 | |
| English Language | 3.7 | |
| Physics | 3.5 | |
| Design | 3.2 | |
| Administration and Management | 3.1 |
Abilities
| Deductive Reasoning | 4.3 | |
| Inductive Reasoning | 4.3 | |
| Oral Comprehension | 4.0 | |
| Written Comprehension | 4.0 | |
| Oral Expression | 4.0 | |
| Written Expression | 4.0 | |
| Problem Sensitivity | 4.0 | |
| Near Vision | 4.0 | |
| Information Ordering | 3.9 | |
| Mathematical Reasoning | 3.9 | |
| Category Flexibility | 3.8 | |
| Fluency of Ideas | 3.6 | |
| Number Facility | 3.6 | |
| Speech Clarity | 3.6 | |
| Flexibility of Closure | 3.5 | |
| Originality | 3.4 | |
| Speech Recognition | 3.4 |
Essential skills
| Reading Comprehension | 4.0 | |
| Critical Thinking | 4.0 | |
| Active Listening | 3.9 | |
| Writing | 3.9 | |
| Speaking | 3.9 | |
| Science | 3.9 | |
| Mathematics | 3.8 | |
| Active Learning | 3.4 | |
| Monitoring | 3.4 | |
| Learning Strategies | 3.1 |
Transferable skills
| Complex Problem Solving | 3.8 | |
| Judgment and Decision Making | 3.8 | |
| Systems Analysis | 3.6 | |
| Systems Evaluation | 3.6 | |
| Operations Analysis | 3.3 |
Skills in demand
Skills employers ask for in job postings for this occupation (Lightcast), with whether each is a common or specialized skill.
Showing the top 40 of 57.
Tools & technology
Showing the top 40 of 89.
Work context
How characteristic each condition is of the job, on O*NET's 1–5 context scale (higher = more present in day-to-day work). Each condition links to how it varies across all occupations.
How to get in
- Job zone
- Zone 4 — Job Zone Four: Considerable Preparation Needed
- Education
- Most of these occupations require a four-year bachelor's degree, but some do not.
- Typical entry-level education
- Bachelor's degree · BLS, the typical path — not a requirement
- Related experience
- A considerable amount of work-related skill, knowledge, or experience is needed for these occupations. For example, an accountant must complete four years of college and work for several years in accounting to be considered qualified.
- Preparation level
- SVP (7.0 to < 8.0) — total schooling plus on-the-job experience.
What to study: Multi/Interdisciplinary Studies , Physical Sciences . Fields of study crosswalked to this occupation (NCES CIP–SOC), not a requirement.
Education of current workers
Share of people in this occupation at each level of education.
| Bachelor's Degree | 60.0% | |
| Master's Degree | 20.0% | |
| Post-Baccalaureate Certificate | 16.0% | |
| Associate's Degree (or other 2-year degree) | 4.0% |
Interests & work styles
The interests and personal qualities O*NET associates with people who do this work.
Career interests (Holland / RIASEC)
| Investigative | 6.1 | |
| Realistic | 4.9 | |
| Conventional | 4.8 |
Interest areas
| Mathematics/Statistics | 5.4 | |
| Physical Science | 5.3 | |
| Information Technology | 5.1 | |
| Engineering | 4.8 | |
| Mechanics/Electronics | 3.2 | |
| Nature/Outdoors | 3.1 | |
| Management/Administration | 2.3 | |
| Agriculture | 2.2 | |
| Life Science | 2.2 |
Work styles
| Dependability | 5.0 | |
| Attention to Detail | 4.0 | |
| Intellectual Curiosity | 3.0 | |
| Innovation | 2.5 |
Wages & employment
U.S. · annual wages (BLS OEWS)
| 10th percentile | $61,990 |
| 25th percentile | $82,450 |
| Median (50th) | $117,960 |
| 75th percentile | $154,900 |
| 90th percentile | $191,880 |
| People employed | 22,580 |
Wages and employment are reported by BLS for the broader occupation group this specialty belongs to (SOC 19-2099), not for the specialty alone.
Industries that employ this occupation
Where these workers are employed, by number of jobs (national, BLS OEWS). Pay shown is the occupation's national median, not industry-specific.
| Industry | Workers | National median pay |
|---|---|---|
| Professional, Scientific, and Technical Services · Sector | 4,280 | $121,490 |
| Educational Services · Sector | 3,700 | $80,130 |
| Manufacturing · Sector | 1,300 | $145,640 |
| Engineering Services · National industry | 530 | $79,160 |
| Management of Companies and Enterprises · Sector | 490 | $168,290 |
| Administrative and Support and Waste Management and Remediation Services · Sector | 220 | $122,660 |
| Utilities · Sector | 120 | $101,560 |
| Testing Laboratories and Services · National industry | 100 | $83,200 |
| Temporary Help Services · National industry | 70 | $100,540 |
| Fossil Fuel Electric Power Generation · National industry | 50 | $114,750 |
| Health Care and Social Assistance · Sector | 40 | $90,920 |
| Other Services (except Public Administration) · Sector | 40 | $114,790 |
Where this work is most concentrated
Industries where this occupation is far more common than in the economy as a whole. The location quotient is how many times more concentrated it is here (a value of 5 means five times its economy-wide share).
| Industry | Concentration | Workers |
|---|---|---|
| Testing Laboratories and Services · National industry | 4.01× | 100 |
| Engineering Services · National industry | 3.13× | 530 |
| Professional, Scientific, and Technical Services · Sector | 2.71× | 4,280 |
| Educational Services · Sector | 1.85× | 3,700 |
| Utilities · Sector | 1.41× | 120 |
| Management of Companies and Enterprises · Sector | 1.19× | 490 |
| Manufacturing · Sector | 0.7× | 1,300 |
| Administrative and Support and Waste Management and Remediation Services · Sector | 0.17× | 220 |
Related occupations
Part of the Energy & Natural Resources career cluster.
- Remote Sensing Technicians · 19-4099.03 Compare →
- Electro-Mechanical and Mechatronics Technologists and Technicians · 17-3024.00 Compare →
- Geographic Information Systems Technologists and Technicians · 15-1299.02 Compare →
- Geological Technicians, Except Hydrologic Technicians · 19-4043.00 Compare →
- Geodetic Surveyors · 17-1022.01 Compare →
- Surveying and Mapping Technicians · 17-3031.00 Compare →
- Calibration Technologists and Technicians · 17-3028.00 Compare →
- Aerospace Engineers · 17-2011.00 Compare →
- Cartographers and Photogrammetrists · 17-1021.00 Compare →
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- Aerospace Engineering and Operations Technologists and Technicians · 17-3021.00 Compare →
Side-by-side comparisons place two occupations’ pay, preparation, skills, and AI exposure on the same page — same data, same scale, no forecast.
What you can do with this
Options the data surfaces for Remote Sensing Scientists and Technologists — not advice or a forecast. Each is a real cross-link you can follow into the evidence.
Skills that travel
Capabilities this work builds that are used across many other occupations.
Adjacent roles
Occupations O*NET rates as related — the nearby moves on the map.
Paths in
How people typically prepare for this work.
- Bachelor's degree · typical entry level
- Job zone 4 — Job Zone Four: Considerable Preparation Needed · preparation tier
- Multi/Interdisciplinary Studies · field of study
- Physical Sciences · field of study
Zoom out
On the global GenAI exposure gradient this work sits around the 78th percentile of 427 international occupations.
Write a report on thisheadline · factoids · citation
Remote Sensing Scientists and Technologists show 74th-percentile AI task overlap — and about 2,000 annual U.S. openings
- Remote Sensing Scientists and Technologists rank in the 74th percentile (High band) for AI task overlap across U.S. occupations — a measure of how much of the work today's AI can attempt, not how much is automated.Eloundou et al. (GPTs are GPTs) + Felten AIOE
- The occupation is projected to see about 2,000 U.S. job openings per year (2024–34), counting growth and replacement — a labor-demand projection made independently of AI.BLS Employment Projections 2024–34
- BLS projects employment to be about average (+0.6%) from 2024 to 2034.BLS Employment Projections 2024–34
- Median annual pay is $117,960, across about 22,580 U.S. workers.BLS OEWS (May 2024)
- Of the AI use actually observed for this work, 28% looks like augmentation (drafting, iterating, checking) rather than hands-off automation — from a Claude.ai usage sample, not a census.2026-01-15-v4-plus-2025-03-27-v2
Copy the whole kit
Remote Sensing Scientists and Technologists show 74th-percentile AI task overlap — and about 2,000 annual U.S. openings • Remote Sensing Scientists and Technologists rank in the 74th percentile (High band) for AI task overlap across U.S. occupations — a measure of how much of the work today's AI can attempt, not how much is automated. (Eloundou et al. (GPTs are GPTs) + Felten AIOE) • The occupation is projected to see about 2,000 U.S. job openings per year (2024–34), counting growth and replacement — a labor-demand projection made independently of AI. (BLS Employment Projections 2024–34) • BLS projects employment to be about average (+0.6%) from 2024 to 2034. (BLS Employment Projections 2024–34) • Median annual pay is $117,960, across about 22,580 U.S. workers. (BLS OEWS (May 2024)) • Of the AI use actually observed for this work, 28% looks like augmentation (drafting, iterating, checking) rather than hands-off automation — from a Claude.ai usage sample, not a census. (2026-01-15-v4-plus-2025-03-27-v2) Source: Singulariki — "Remote Sensing Scientists and Technologists". https://singulariki.com/roles/role-19-2099-01 Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.
AssetsShare imageMethodology & sourcesPress & newsroomThe newsroom
Every line is built only from figures this page already shows and cites. AI task overlap means what today's AI can attempt — not automation, job loss, or a forecast.
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
- BLS Occupational Employment and Wage Statistics (OEWS) May 2024 U.S. Bureau of Labor Statistics
- BLS Employment Projections 2024–2034 U.S. Bureau of Labor Statistics
- Anthropic Economic Index v4 (2026-01-15) + v2 (2025-03-27) Anthropic
- Microsoft “Working with AI” working-with-ai Microsoft Research
- “GPTs are GPTs” (Eloundou et al.) arXiv 2303.10130 OpenAI / academic
- AI Occupational Exposure (AIOE) Felten, Raj & Seamans academic
- ILO / Gmyrek et al. GenAI exposure gradient 2025 International Labour Organization
- IBS O*NET-SOC ↔ ISCO-08 occupation crosswalk 2022 Institute for Structural Research (IBS)
- Frey & Osborne (2013) frey-osborne-automation academic
- Dingel & Neiman (2020) dingel-neiman-workathome academic
Data compiled June 2, 2026. Figures are estimates, not advice.
Cite this page
Plain
Singulariki. "Remote Sensing Scientists and Technologists." Singulariki: a source-backed encyclopedia of work. Built from O*NET 30.3; BLS Occupational Employment and Wage Statistics (OEWS) May 2024; BLS Employment Projections 2024–2034; Anthropic Economic Index v4 (2026-01-15) + v2 (2025-03-27); Microsoft “Working with AI” working-with-ai; “GPTs are GPTs” (Eloundou et al.) arXiv 2303.10130; AI Occupational Exposure (AIOE) Felten, Raj & Seamans; ILO / Gmyrek et al. GenAI exposure gradient 2025; IBS O*NET-SOC ↔ ISCO-08 occupation crosswalk 2022; Frey & Osborne (2013) frey-osborne-automation; Dingel & Neiman (2020) dingel-neiman-workathome. Accessed June 7, 2026. https://singulariki.com/roles/role-19-2099-01
APA
Singulariki. (2026). Remote Sensing Scientists and Technologists. Singulariki: a source-backed encyclopedia of work. Retrieved June 7, 2026, from https://singulariki.com/roles/role-19-2099-01
BibTeX
@misc{singulariki-role-19-2099-01,
title = {Remote Sensing Scientists and Technologists},
author = {{Singulariki}},
year = {2026},
note = {O*NET 30.3; BLS Occupational Employment and Wage Statistics (OEWS) May 2024; BLS Employment Projections 2024–2034; Anthropic Economic Index v4 (2026-01-15) + v2 (2025-03-27); Microsoft “Working with AI” working-with-ai; “GPTs are GPTs” (Eloundou et al.) arXiv 2303.10130; AI Occupational Exposure (AIOE) Felten, Raj & Seamans; ILO / Gmyrek et al. GenAI exposure gradient 2025; IBS O*NET-SOC ↔ ISCO-08 occupation crosswalk 2022; Frey & Osborne (2013) frey-osborne-automation; Dingel & Neiman (2020) dingel-neiman-workathome. Accessed June 7, 2026},
url = {https://singulariki.com/roles/role-19-2099-01}
} Citations name the underlying public dataset releases — they reflect what this page is built from, not just the URL.