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.
U.S. Workers
22,580
Median Salary
$117,960
10-Year Growth
+0.6%
Annual Openings
2,000
Typical entry: Bachelor's degree
22 of 24 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.
Manage or analyze data obtained from remote sensing systems to obtain meaningful results.
AI: Fully automatable - Managing and analyzing remote sensing datasets is highly automatable through established data pipelines, cloud processing, and ML analysis that produce actionable results in 2025.
Analyze data acquired from aircraft, satellites, or ground-based platforms, using statistical analysis software, image analysis software, or Geographic Information Systems (GIS).
AI: Fully automatable - Analysis of airborne, satellite, and ground-based sensor data using statistical, image-analysis, and GIS tools is routinely automated and scalable with existing software and ML methods.
Process aerial or satellite imagery to create products such as land cover maps.
AI: Fully automatable - Processing aerial and satellite imagery into products like land cover maps is widely automated through supervised and unsupervised classification workflows and cloud processing services.
Organize and maintain geospatial data and associated documentation.
AI: Fully automatable - Automated tools and AI can index, validate, metadata-tag, backup and version-control geospatial datasets end-to-end, making this largely automatable with minimal human intervention.
Apply remote sensing data or techniques, such as surface water modeling or dust cloud detection, to address environmental issues.
AI: Fully automatable - AI systems in 2025 can apply remote sensing techniques—e.g., water modeling and dust detection—at operational quality for many environmental applications, though complex policy or novel research contexts may need human oversight.
Develop automated routines to correct for the presence of image distorting artifacts, such as ground vegetation.
AI: Fully automatable - By 2025, ML and image-processing pipelines can fully design and implement automated artifact-correction routines given adequate training/validation data.
Compile and format image data to increase its usefulness.
AI: Fully automatable - Data compilation, formatting, and standard preprocessing of imagery are routine ETL tasks that AI systems and pipelines can fully automate.
Use remote sensing data for forest or carbon tracking activities to assess the impact of environmental change.
AI: Fully automatable - AI-driven remote sensing analysis can fully perform forest mapping, biomass/carbon estimation, and trend assessment given appropriate models and input data.
Design or implement strategies for collection, analysis, or display of geographic data.
AI: Partial - AI can generate and evaluate strategies for geographic data collection, analysis, and display, but strategic planning requires domain knowledge, stakeholder alignment, and contextual trade-offs that still need human leadership.
Integrate other geospatial data sources into projects.
AI: Partial - AI can automate ingestion, reprojection, and basic harmonization of geospatial datasets but still needs human judgment for complex provenance, semantics, and nonstandard formats.
Discuss project goals, equipment requirements, or methodologies with colleagues or team members.
AI: Partial - AI can draft agendas and simulate discussions but cannot fully replace human negotiation, tacit knowledge sharing, and real-time collaborative decision-making.
Develop or build databases for remote sensing or related geospatial project information.
AI: Partial - AI can design schemas, generate code, and automate deployments for geospatial databases but human oversight is still required for governance, security, and bespoke integration.
Collect supporting data, such as climatic or field survey data, to corroborate remote sensing data analyses.
AI: Partial - AI can automatically harvest existing climatic and remote-sensed datasets but cannot perform physical field surveys, so full corroboration that requires new field data remains manual.
Prepare or deliver reports or presentations of geospatial project information.
AI: Partial - AI can generate high-quality reports and slide decks and even create recordings, but live, interactive presentation and stakeholder engagement typically require a human presenter.
Conduct research into the application or enhancement of remote sensing technology.
AI: Partial - AI accelerates literature review, idea generation, and experiment automation but cannot independently lead original experimental research or interpret ambiguous results without human researchers.
Train technicians in the use of remote sensing technology.
AI: Partial - AI can deliver much of the theoretical and simulated training for remote sensing technicians, but hands-on equipment use, mentorship, and troubleshooting in the field still need human trainers.
Attend meetings or seminars or read current literature to maintain knowledge of developments in the field of remote sensing.
AI: Partial - AI can continuously monitor, summarize, and extract insights from literature and virtual talks, but attending and actively participating in meetings or in-person seminars remains a human role.
Monitor quality of remote sensing data collection operations to determine if procedural or equipment changes are necessary.
AI: Partial - AI can continuously monitor telemetry and flag quality issues and suggest adjustments, but final procedural or equipment change decisions typically require human judgment and accountability.
Develop new analytical techniques or sensor systems.
AI: Partial - AI can generate and optimize novel analytical approaches and sensor designs, but truly novel conceptual breakthroughs and hardware innovation still rely on human-led R&D and experimental validation.
Set up or maintain remote sensing data collection systems.
AI: Partial - Software configuration, remote diagnostics, and routine maintenance can be automated, but physical setup and complex field maintenance remain dependent on humans or specialized robotics.
Direct installation or testing of new remote sensing hardware or software.
AI: Partial - AI can plan, coordinate, and run many installation and testing procedures (especially software), but overseeing complex hardware installation and on-site testing still requires human oversight.
Recommend new remote sensing hardware or software acquisitions.
AI: Partial - AI can analyze needs, performance data, and costs to recommend hardware/software options, but procurement decisions need human contextual judgment and organizational approval.
Participate in fieldwork.
AI: Not automatable - Fieldwork involves on-site, physical tasks and complex judgment that AI cannot perform autonomously as of 2025.
Direct all activity associated with implementation, operation, or enhancement of remote sensing hardware or software.
AI: Not automatable - Directing all implementation and operational activities entails leadership, responsibility, cross-team coordination, and accountability that AI cannot fully assume.