Remote Sensing Scientists and Technologists
Life, Physical, and Social Science Occupations- O*NET-SOC code
- 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.
- Data source: BLSPublished: 2026-08
High· relative
LowFour relative bandsVery highGroup-level value
Scale, basis and source
Four relative bands (Low / Moderate / High / Very high)
831 detailed occupations in the BLS Employment Projections table. Assigned per National Employment Matrix (NEM) code, so occupations sharing a NEM code carry the same band
- Data source: AnthropicPublished: 2026-03
0.038
0.000Range of values carried here0.745Group-level value
Scale, basis and source
Observed exposure index, 0–1 as published
Mapped onto O*NET tasks
Published per SOC 2018 occupation; every O*NET occupation with the same SOC 2018 code carries this value
- Data source: OpenAIPublished: 2023
0.446
0.000Range of values carried here0.844Scale, basis and source
What kind of figure this source publishes
The BLS category is a relative rank, not an absolute level, and it is not a first-hand measurement: it groups an occupation's percentile ranks across several published studies into four bands. It is not an employment or wage forecast, not a probability of adoption, and it does not separate automation from augmentation.
Task-level exposure
Show 22 hidden tasks24 rated tasks · 22 tasks with β ≥ 0.5 (91.7%)
β = direct exposure (E1) + 0.5 × exposure when tools are available (E2), per the source repository's definition.
- Source unit: O*NET 27.2 tasks → O*NET 31.0 occupation code
- All rated tasks are in the O*NET 31.0 task list.
- Source
- OpenAI "GPTs are GPTs" exposure rubric
- Release
- gh-main-0471612
- License
- MIT License, Copyright (c) 2024 OpenAI
| Task (O*NET 20.1) | Claude.aiRaw / share % | APIRaw / share % |
|---|---|---|
Compile and format image data to increase its usefulness.19-2099.01 | 0.014888.1 | 0.064692.4 |
Organize and maintain geospatial data and associated documentation.19-2099.01 | 0.002011.9 | 0.00547.6 |
Values in this tab are predicted labels, not observations. Eloundou et al. (2023) published two rating regimes — human raters and GPT-4 — and the β shown here is derived from the GPT-4 rater basis alone; the same task can take a different value under the other regime. The unit and the meaning differ from the observed shares (%) in the other tabs, so do not place them on the same axis.
| Task (O*NET 27.2) | βE1 + 0.5 × E2 |
|---|---|
Analyze data acquired from aircraft, satellites, or ground-based platforms, using statistical analysis software, image analysis software, or Geographic Information Systems (GIS).O*NET Task ID 18256 | 0.5 |
Develop or build databases for remote sensing or related geospatial project information.O*NET Task ID 18257 | 0.5 |
Integrate other geospatial data sources into projects.O*NET Task ID 18258 | 0.5 |
Prepare or deliver reports or presentations of geospatial project information.O*NET Task ID 18259 | 0.5 |
Organize and maintain geospatial data and associated documentation.O*NET Task ID 18260 | 0.5 |
Process aerial or satellite imagery to create products such as land cover maps.O*NET Task ID 18261 | 0.5 |
Design or implement strategies for collection, analysis, or display of geographic data.O*NET Task ID 18262 | 0.5 |
Direct all activity associated with implementation, operation, or enhancement of remote sensing hardware or software.O*NET Task ID 18263 | 0.5 |
Collect supporting data, such as climatic or field survey data, to corroborate remote sensing data analyses.O*NET Task ID 18264 | 0.5 |
Compile and format image data to increase its usefulness.O*NET Task ID 18265 | 0.5 |
Conduct research into the application or enhancement of remote sensing technology.O*NET Task ID 18266 | 0.5 |
Develop automated routines to correct for the presence of image distorting artifacts, such as ground vegetation.O*NET Task ID 18268 | 0.5 |
Develop new analytical techniques or sensor systems.O*NET Task ID 18269 | 0.5 |
Manage or analyze data obtained from remote sensing systems to obtain meaningful results.O*NET Task ID 18270 | 0.5 |
Monitor quality of remote sensing data collection operations to determine if procedural or equipment changes are necessary.O*NET Task ID 18271 | 0.5 |
Direct installation or testing of new remote sensing hardware or software.O*NET Task ID 18272 | 0.5 |
Attend meetings or seminars or read current literature to maintain knowledge of developments in the field of remote sensing.O*NET Task ID 18273 | 0.5 |
Recommend new remote sensing hardware or software acquisitions.O*NET Task ID 18275 | 0.5 |
Set up or maintain remote sensing data collection systems.O*NET Task ID 18276 | 0.5 |
Train technicians in the use of remote sensing technology.O*NET Task ID 18277 | 0.5 |
Apply remote sensing data or techniques, such as surface water modeling or dust cloud detection, to address environmental issues.O*NET Task ID 19773 | 0.5 |
Use remote sensing data for forest or carbon tracking activities to assess the impact of environmental change.O*NET Task ID 19774 | 0.5 |
β = E1 + 0.5 × E2 · E1 = tasks where direct LLM access alone cuts time by at least 50%, E2 = tasks where software built on top of an LLM cuts time by at least 50%. Values take only 0 / 0.5 / 1.0.
Data sources & licenses — O*NET®, Anthropic Economic Index, Eloundou et al. (2023): see full notices on the Credits page
All O*NET 31.0 task statements (24)
All O*NET 31.0 task statements for this occupation (English, as published; task type core or supplemental). Exposure values are not listed here; they are in the table above, next to each task statement as worded in the O*NET version each source used.
| Task (O*NET 31.0) | Type |
|---|---|
| Analyze data acquired from aircraft, satellites, or ground-based platforms, using statistical analysis software, image analysis software, or Geographic Information Systems (GIS). | Core |
| Develop or build databases for remote sensing or related geospatial project information. | Core |
| Integrate other geospatial data sources into projects. | Core |
| Prepare or deliver reports or presentations of geospatial project information. | Core |
| Organize and maintain geospatial data and associated documentation. | Core |
| Process aerial or satellite imagery to create products such as land cover maps. | Core |
| Design or implement strategies for collection, analysis, or display of geographic data. | Core |
| Direct all activity associated with implementation, operation, or enhancement of remote sensing hardware or software. | Core |
| Collect supporting data, such as climatic or field survey data, to corroborate remote sensing data analyses. | Core |
| Compile and format image data to increase its usefulness. | Core |
| Conduct research into the application or enhancement of remote sensing technology. | Core |
| Discuss project goals, equipment requirements, or methodologies with colleagues or team members. | Core |
| Develop automated routines to correct for the presence of image distorting artifacts, such as ground vegetation. | Core |
| Develop new analytical techniques or sensor systems. | Core |
| Manage or analyze data obtained from remote sensing systems to obtain meaningful results. | Core |
| Monitor quality of remote sensing data collection operations to determine if procedural or equipment changes are necessary. | Core |
| Direct installation or testing of new remote sensing hardware or software. | Core |
| Attend meetings or seminars or read current literature to maintain knowledge of developments in the field of remote sensing. | Core |
| Participate in fieldwork. | Core |
| Recommend new remote sensing hardware or software acquisitions. | Core |
| Set up or maintain remote sensing data collection systems. | Core |
| Train technicians in the use of remote sensing technology. | Core |
| Apply remote sensing data or techniques, such as surface water modeling or dust cloud detection, to address environmental issues. | Core |
| Use remote sensing data for forest or carbon tracking activities to assess the impact of environmental change. | Core |
0.07/ 3· Robot exposure index
Scale, basis and source
Each physical task is graded 0–3 by the setting in which robots that have been deployed, sold or demonstrated could do it (0 none · 1 purpose-built robot settings such as a fenced machine cell or a bottling line · 2 structured facilities such as a hospital pharmacy or a container port · 3 unstructured settings such as a private home or a construction site), and the grades are averaged by share of working time
Physical tasks among this occupation's O*NET 29.3 tasks
Anthropic estimated the grades and time shares with Claude Opus 5 using web search, as of 2026.
The robot exposure index is a different measure from the AI exposure figures on this site and cannot be added to or compared with them. It does not measure actual adoption or cost, and 0 also covers occupations with no physical tasks.
Tier of each physical task
Robot environment tiers (E0–E3)
E0 noneDetails
Rated not doable by robots that have been deployed, sold or demonstrated, even in a purpose-built setting
E1 purpose-built robot settingsDetails
Rated doable in a setting built around the machine, such as a fenced machine cell or a bottling line
E2 structured facilitiesDetails
Rated doable in a human work facility that an institution keeps to a working standard, such as a hospital pharmacy or a container port
E3 unstructured settingsDetails
Rated doable in a setting that no institution keeps to a working standard, such as a private home or a construction site
Only tasks classified as physical are listed; other tasks have no tier in the source.
| Task (O*NET 29.3) | Robot environment tier |
|---|---|
| Participate in fieldwork. | E3 unstructured settings |
| Set up or maintain remote sensing data collection systems. | E0 none |
Occupation information
Employment outlook (BLS)
Values for the whole SOC 19-2099 group (Physical scientists, all other), not for this O*NET occupation alone.
- Employment, 2025
- 27K
- Projected employment, 2035
- 27K
- Change (2025–35)
- +2.1%
- Median annual wage, 2025
- $122,570
- Typical education needed for entry
- Bachelor's degree
Korea classification links (KSCO)
Links from the current release of the Korea Occupation Direct Crosswalk v1.1, made by AI Changing Work. Relation values are read from the O*NET-SOC occupation side.
| KSCO code | KSCO title | Relation | Status |
|---|---|---|---|
| 23162 | 사진측량 및 원격탐사 분석가 | Same scope | confirmed |
Relation and status definitions(kr-direct v1.1)
ESCO occupational vocabulary
Each entry below is matched to this occupation in the published ESCO-O*NET Crosswalk (version 1, 2022). The match types are that crosswalk's; the wording is AI Changing Work's.
- geographerESCO 2632.5Overlapping, not the same
Geographers are scholars who study human and physical geography. Depending on their specialisation, they study political, economical and cultural aspects of humanity contained within human geography. Moreover, they study land formations, soils, natural frontiers, and water flows contained in physical geography.
- remote sensing technicianESCO 3111.13Overlapping, not the same
Remote sensing technicians collect airborne data. They utilise equipment aimed for the collection of data and determination of geographical points in order to help in a variety of operations such as land conservation, urban planning, and military operations.
- seismologistESCO 2114.1.10Overlapping, not the same
Seismologists study the movement of tectonic plaques in the Earth which causes the propagation of seismic waves and earthquakes. They study and observe the various sources causing earthquakes such as volcanic activity, atmospheric phenomena, or the behaviour of oceans. They provide their scientific observations to prevent dangers in construction and infrastructure.
Source: European Commission, DG EMPL — ESCO API 2026-08-08 (bridge v1.2.1)
License: CC BY 4.0 (EU Reuse Notice)
ESCO reuse terms and the modification notice are in Credits.
France classification links (ROME)
AI Changing Work's links between this occupation and ROME 4.0 job profiles, judged with AI models. Relations are read from this occupation's side. Links marked “provisional” were adopted on the judgments of two judges from the same model family (Claude) alone, or by an operator rule applied to their judgments, so they rest on weaker evidence than confirmed links.
| ROME code | ROME title (French) | Relation |
|---|---|---|
| M1808 | Cartographe | Related, not a match |
| M1895 | Géomaticien / Géomaticienne | Related, not a match |
Source: France Travail — Répertoire Opérationnel des Métiers et des Emplois (ROME 4.0), version 61, last updated 15 June 2026 (https://www.data.gouv.fr/datasets/repertoire-operationnel-des-metiers-et-des-emplois-rome). Licence Ouverte 2.0.
ROME codes and titles are reproduced unchanged; the links to O*NET-SOC occupations are AI Changing Work's own judgment and are not a France Travail correspondence.
(AI Changing Work link release: fr-direct v1)
Spain classification links (CNO-11)
AI Changing Work's links between this occupation and CNO-11 occupations, judged with AI models. Relations are read from this occupation's side.
| CNO-11 code | CNO-11 title (Spanish) | Relation |
|---|---|---|
| 2414 | Geólogos y geofísicos | Related, not a match |
| 2454 | Ingenieros geógrafos y cartógrafos | Related, not a match |
| 2821 | Sociólogos, geógrafos, antropólogos, arqueólogos y afines | Related, not a match |
Source: Instituto Nacional de Estadística (INE), National Classification of Occupations 2011 (CNO-11, Royal Decree 1591/2010) — www.ine.es · Licensed CC BY 4.0. CNO-11 codes and Spanish titles are reproduced unchanged; the links to O*NET occupations are AI Changing Work's own judgment and are not an INE correspondence.
(AI Changing Work link release: es-direct v1)
Germany classification links (KldB)
AI Changing Work's links between this occupation and KldB occupations, judged with AI models. Relations are read from this occupation's side.
| KldB code | KldB title (German) | Relation |
|---|---|---|
| 43144 | Berufe in der Geoinformatik - hoch komplexe Tätigkeiten | Overlapping, not the same |
| 42134 | Berufe in der Geografie - hoch komplexe Tätigkeiten | Related, not a match |
Source: © Statistik der Bundesagentur für Arbeit — Klassifikation der Berufe 2010 – überarbeitete Fassung 2020, https://statistik.arbeitsagentur.de. KldB codes and titles are reproduced unchanged; the links to O*NET occupations are AI Changing Work's own mapping (eigene Zuordnung) and are not a Bundesagentur für Arbeit correspondence.
(AI Changing Work link release: de-direct v1)
Japan classification links (JSCO)
AI Changing Work's links between this occupation and JSCO occupations, judged with AI models. Relations are read from this occupation's side.
| JSCO code | JSCO title (Japanese) | Relation |
|---|---|---|
| 051 | 自然科学系研究者 | Related, not a match |
| 093 | 測量技術者 | Related, not a match |
Source: Ministry of Internal Affairs and Communications (MIC), Japan Standard Occupational Classification (Dec. 2009, MIC Notification No. 555). JSCO codes and Japanese titles are reproduced unchanged; the links to O*NET occupations are AI Changing Work's own judgment and are not a MIC correspondence.
(AI Changing Work link release: jp-direct v1)
Sources, licenses and AI Changing Work's modification and translation notices for this page: Credits & sources