Remote Sensing Scientists and Technologists
Life, Physical, and Social Science Occupations- Código O*NET-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.
Los nombres de las ocupaciones y las descripciones de tareas se muestran en inglés, tal como se publicaron. Las etiquetas, incluidos los tipos de tarea, están traducidas.
Exposición a la IA
- Fuente de datos: BLSPublicado: 2026-08
Alto· relativa
BajoCuatro bandas relativasMuy altoValor por grupo ocupacional
Escala, base y fuente
Cuatro bandas relativas (Bajo / Moderado / Alto / Muy alto)
831 ocupaciones detalladas de la tabla de proyecciones de empleo de BLS. El valor se asigna por código de la National Employment Matrix (NEM), de modo que las ocupaciones que comparten un código NEM reciben la misma banda
- Fuente de datos: AnthropicPublicado: 2026-03
0.038
0.000Rango de los valores aquí recogidos0.745Valor por grupo ocupacional
Escala, base y fuente
Índice de exposición observada, 0–1 tal como se publica
Mapeado sobre tareas de O*NET
Publicado por ocupación SOC 2018; toda ocupación O*NET con el mismo código SOC 2018 recibe este valor
- Fuente de datos: OpenAIPublicado: 2023
0.446
0.000Rango de los valores aquí recogidos0.844Escala, base y fuente
β de evaluadores humanos, 0–1 tal como se publica
Mapeado sobre tareas de O*NET
Qué tipo de cifra publica esta fuente
La categoría de BLS es un rango relativo, no un nivel absoluto, y tampoco es una medición de primera mano: agrupa en cuatro bandas los rangos percentiles de la ocupación en varios estudios publicados. No es una previsión de empleo ni de salarios, no es una probabilidad de adopción y no distingue entre automatización y aumento.
Exposición a la IA (rúbrica de OpenAI)
24 tareas evaluadas · 22 tareas con β ≥ 0,5 (91.7%)
β = exposición directa (E1) + 0,5 × exposición con herramientas disponibles (E2), según la definición del repositorio de origen.
- Unidad de origen: tareas de O*NET 27.2 → código de ocupación de O*NET 31.0
- Todas las tareas puntuadas figuran en la lista de tareas de O*NET 31.0.
- Fuente
- OpenAI "GPTs are GPTs" exposure rubric
- Versión
- gh-main-0471612
- Licencia
- MIT License, Copyright (c) 2024 OpenAI
Tareas
Descripciones de tareas de la O*NET® 31.0 Database, primero las tareas principales.
| Tarea | Tipo | β (OpenAI) |
|---|---|---|
| Analyze data acquired from aircraft, satellites, or ground-based platforms, using statistical analysis software, image analysis software, or Geographic Information Systems (GIS). | Principal | 0,5 |
| Develop or build databases for remote sensing or related geospatial project information. | Principal | 0,5 |
| Integrate other geospatial data sources into projects. | Principal | 0,5 |
| Prepare or deliver reports or presentations of geospatial project information. | Principal | 0,5 |
| Organize and maintain geospatial data and associated documentation. | Principal | 0,5 |
| Process aerial or satellite imagery to create products such as land cover maps. | Principal | 0,5 |
| Design or implement strategies for collection, analysis, or display of geographic data. | Principal | 0,5 |
| Direct all activity associated with implementation, operation, or enhancement of remote sensing hardware or software. | Principal | 0,5 |
| Collect supporting data, such as climatic or field survey data, to corroborate remote sensing data analyses. | Principal | 0,5 |
| Compile and format image data to increase its usefulness. | Principal | 0,5 |
| Conduct research into the application or enhancement of remote sensing technology. | Principal | 0,5 |
| Discuss project goals, equipment requirements, or methodologies with colleagues or team members. | Principal | 0 |
| Develop automated routines to correct for the presence of image distorting artifacts, such as ground vegetation. | Principal | 0,5 |
| Develop new analytical techniques or sensor systems. | Principal | 0,5 |
| Manage or analyze data obtained from remote sensing systems to obtain meaningful results. | Principal | 0,5 |
| Monitor quality of remote sensing data collection operations to determine if procedural or equipment changes are necessary. | Principal | 0,5 |
| Direct installation or testing of new remote sensing hardware or software. | Principal | 0,5 |
| Attend meetings or seminars or read current literature to maintain knowledge of developments in the field of remote sensing. | Principal | 0,5 |
| Participate in fieldwork. | Principal | 0 |
| Recommend new remote sensing hardware or software acquisitions. | Principal | 0,5 |
| Set up or maintain remote sensing data collection systems. | Principal | 0,5 |
| Train technicians in the use of remote sensing technology. | Principal | 0,5 |
| Apply remote sensing data or techniques, such as surface water modeling or dust cloud detection, to address environmental issues. | Principal | 0,5 |
| Use remote sensing data for forest or carbon tracking activities to assess the impact of environmental change. | Principal | 0,5 |
Información ocupacional
Fuentes y atribución
This page includes information from the O*NET® 31.0 Database (https://www.onetcenter.org/database.html) by the U.S. Department of Labor, Employment and Training Administration (USDOL/ETA). Used under the CC BY 4.0 license (https://creativecommons.org/licenses/by/4.0/). O*NET® is a trademark of USDOL/ETA. AI Changing Work has modified all or some of this information: the O*NET-SOC code, title and task statements are reproduced in English without change; task-type labels are shown in the page's language and tasks are listed core first; any Korean occupation title shown on the Korean-language page is AI Changing Work's translation; any KSCO-8 unit groups linked to this occupation were paired with it by AI Changing Work's judgment, and the relation labels and statuses are AI Changing Work's additions. USDOL/ETA has not approved, endorsed, or tested these modifications.
Any AI exposure figures on this page are published by third parties, not by AI Changing Work, and none is part of the O*NET information. OpenAI publishes task-level scores (MIT License) for O*NET 27.2 task statements; each is shown next to the O*NET 31.0 task statement with the same task ID, whose wording can differ from the 27.2 statement that was scored. OpenAI also publishes occupation-level scores for O*NET-SOC codes in the same release, and any such score is shown on the O*NET occupation with the same code. Anthropic publishes an observed exposure index in the Anthropic Economic Index (CC-BY), and the U.S. Bureau of Labor Statistics publishes relative AI exposure categories (public domain); both are published per SOC code, and each value is shown on every O*NET occupation with that code. The International Labour Organization publishes a generative AI exposure index in ILO Working Paper 140 (CC BY 4.0) for ISCO-08 unit groups; AI Changing Work links those groups to O*NET occupations by applying the U.S. Bureau of Labor Statistics ISCO-08 to 2010 SOC and 2010 SOC to 2018 SOC crosswalks as published, without case-by-case selection, and these crosswalks match many groups only in part. Where several unit groups are linked, each group's published value is listed, and any summary shows only the lowest and highest of those values with the number of groups; no exposure figure is averaged or recalculated. Any employment figures are published by the U.S. Bureau of Labor Statistics for the SOC group containing this occupation. Each source is credited where its figures are shown.
O*NET OnLine: 19-2099.01 Remote Sensing Scientists and Technologists
KSCO 코드·명칭: 한국표준직업분류(제8차 개정) — 통계청 고시 제2024-328호 (2024-07-01 고시, 2025-01-01 시행). 저작권법 제7조 제2호의 고시 항목이다. 명칭 표기(가운뎃점·띄어쓰기)는 해설서 2차 정오 반영판의 표기를 따랐으며, 고시 항목표와는 18개 명칭에서 가운뎃점 글리프나 띄어쓰기만 다르다. 통계청은 2025년 10월 국가데이터처로 개편되었다. 이 페이지의 KSCO 연결은 통계청·국가데이터처의 공식 연계표가 아니다.