Apply remote sensing technologies to assist scientists in areas such as natural resources, urban planning, or homeland security. May prepare flight plans or sensor configurations for flight trips.
U.S. Workers
71,400
Median Salary
$60,130
10-Year Growth
+3.5%
Annual Openings
10,600
Typical entry: Associate's degree
22 of 22 tasks have some AI capability
Exposure Trend
This score reflects estimated AI technical capability for tasks in this occupation. It does not predict employment changes, and it does not account for company-specific constraints, regulation, or adoption barriers.
Correct raw data for errors due to factors such as skew or atmospheric variation.
AI: Fully automatable - Geometric and atmospheric correction algorithms and radiative transfer models are mature and can be automated to correct skew and atmospheric effects for most datasets.
Integrate remotely sensed data with other geospatial data.
AI: Fully automatable - Mature geospatial tools and automated pipelines can reproject, align, transform, and merge remotely sensed and other geospatial datasets end-to-end for most integration tasks.
Adjust remotely sensed images for optimum presentation by using software to select image displays, define image set categories, or choose processing routines.
AI: Fully automatable - Image enhancement, display selection, category definition, and processing-routine selection are routine tasks that current image-processing software and ML models can perform automatically.
Manipulate raw data to enhance interpretation, either on the ground or during remote sensing flights.
AI: Fully automatable - Automated preprocessing and enhancement algorithms, including onboard/real-time processing, can manipulate raw sensor data to improve interpretability in routine and operational contexts.
Merge scanned images or build photo mosaics of large areas, using image processing software.
AI: Fully automatable - Mosaicking and stitching of scanned images at scale are well-established automated processes implemented in current photogrammetry and image-processing software.
Monitor raw data quality during collection and make equipment corrections as necessary.
AI: Fully automatable - Recording and maintaining survey metadata and records is routine and can be fully automated with existing data pipelines, logging systems, and database tools.
Maintain records of survey data.
AI: Fully automatable - Maintaining records of survey and sensor data is routine data management that can be fully automated with AI/RPA and database integrations.
Document methods used and write technical reports containing information collected.
AI: Fully automatable - Modern LLMs and document-generation systems can compile methods and produce technical reports from logs and data with high fidelity, making routine reporting fully automatable.
Prepare documentation or presentations, including charts, photos, or graphs.
AI: Fully automatable - Preparation of documentation and presentations (charts, photos, graphs) from data is a straightforward task that AI and visualization tools can generate end-to-end.
Provide remote sensing data for use in addressing environmental issues, such as surface water modeling or dust cloud detection.
AI: Fully automatable - By 2025 AI systems can ingest satellite/airborne imagery and produce processed remote-sensing data products (surface-water extents, dust plume detections, geospatial layers) end-to-end for environmental use.
Collect remote sensing data for forest or carbon tracking activities involved in assessing the impact of environmental change.
AI: Fully automatable - Acquisition of remote sensing data for forest and carbon monitoring is largely automatable via satellite tasking and UAV workflows, enabling routine collection without continual human involvement.
Collect geospatial data, using technologies such as aerial photography, light and radio wave detection systems, digital satellites, or thermal energy systems.
AI: Partial - Automated drones, satellites, and sensor systems can perform geospatial data collection under AI control, but physical deployment, regulatory compliance and some manual oversight prevent full automation.
Verify integrity and accuracy of data contained in remote sensing image analysis systems.
AI: Partial - AI can run automated quality checks and anomaly detection to assess data integrity and flag likely inaccuracies, but full verification often requires expert judgment and ground-truth validation.
Consult with remote sensing scientists, surveyors, cartographers, or engineers to determine project needs.
AI: Partial - Consultation requires understanding stakeholder goals, tradeoffs, and coordination across disciplines; AI can prepare materials and recommendations but cannot fully replace human experts in negotiation and project scoping.
Calibrate data collection equipment.
AI: Partial - While AI can guide calibration, suggest parameter settings, and perform software-based calibration, physical sensor calibration often requires on-site procedures and human technicians.
Develop or maintain geospatial information databases.
AI: Partial - AI and automation tools can ingest, organize, and maintain geospatial databases at scale but still require human oversight for schema design, quality control, and access/security decisions.
Participate in the planning or development of mapping projects.
AI: Partial - Participating in planning and development of mapping projects requires cross-disciplinary judgment, prioritization, and stakeholder engagement that AI can support but not fully undertake.
Operate airborne remote sensing equipment, such as survey cameras, sensors, or scanners.
AI: Partial - Autopilot systems and automated sensor control enable much of airborne sensor operation, but safe, compliant operation and complex in-flight decisions still typically need human supervision.
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.
AI: Partial - AI can rapidly generate equipment and software recommendations from requirements and past projects, but final selection and trade-off judgments still depend on human expertise and context knowledge.
Collect verification data on the ground, using equipment such as global positioning receivers, digital cameras, or notebook computers.
AI: Partial - Automated tools and mobile apps can capture ground verification data, yet many collection scenarios require human presence, judgment, or adaptive sampling that AI cannot fully shoulder.
Develop specialized computer software routines to customize and integrate image analysis.
AI: Partial - AI coding assistants can generate and prototype specialized image-analysis routines, but developing, debugging, and integrating robust, production-grade custom software still requires human developers.
Collaborate with agricultural workers to apply remote sensing information to efforts to reduce negative environmental impacts of farming practices.
AI: Partial - AI can analyze data and propose interventions for agricultural workers, but effective collaboration, trust-building, and context-specific implementation require human-to-human engagement.