AI in geospatial intelligence: What government leaders need to know
Understanding the technology behind faster, more trusted intelligence
Outcomes Unlocked: Charting the Unknown
What you'll learn
- How AI is helping analysts process geospatial data faster without replacing human expertise
- Why trusted human-machine teaming is essential for defense and intelligence missions
- How Leidos' RAVe technology is modernizing nautical chart production and improving data quality
This article is part of the Outcomes Unlocked series exploring how artificial intelligence is solving complex government and defense challenges.
Artificial intelligence (AI) is helping government and defense organizations process geospatial intelligence (GEOINT) faster, improve data quality, and make more informed decisions.
As satellites, sensors, and mapping systems generate unprecedented volumes of data, analysts face a growing challenge: turning information into trusted intelligence quickly enough to support the mission. AI is helping meet that challenge by automating repetitive tasks, accelerating production, and allowing analysts to focus on the work that requires human expertise.
The goal is not to replace analysts, it's to help them deliver trusted intelligence faster.
What is geospatial intelligence (GEOINT)?
Geospatial intelligence (GEOINT) combines imagery, mapping, navigation data, and other location-based information to create a detailed understanding of the physical environment.
Government agencies use GEOINT to support defense operations, maritime safety, intelligence analysis, disaster response, and critical infrastructure protection. As the volume of geospatial data continues to grow, organizations need new ways to process and analyze that information at operational speed.
How is AI used in geospatial intelligence?
AI helps analysts process more information in less time by automating repetitive, manual tasks.
Today, AI can assist with:
- Detecting features within imagery
- Converting maps and charts into structured geospatial data
- Identifying changes across large datasets
- Prioritizing information for analyst review
- Accelerating chart production
- Improving production consistency
By automating repetitive work, AI allows analysts to spend more time validating results, applying operational context and supporting mission decisions.
What is RAVe?
RAVe (Raster Automation to Vector) is a Leidos-developed capability that converts raster maps and charts into structured geospatial vector data.
Vector data is easier to analyze, update, search, and integrate into operational systems than pixel-based raster imagery, making it more useful for intelligence and navigation workflows.
Traditionally, analysts manually traced thousands of individual chart features—a time-consuming process that required extensive quality control. RAVe automates much of that work by identifying chart elements and converting them into accurate, georeferenced vector data for analyst review.
RAVe acts as a force multiplier, allowing experts to review, validate and refine outputs far more efficiently.
How does AI improve chart production?
Producing nautical charts requires identifying and digitizing thousands of individual features, including coastlines, depth contours, navigation channels, submerged hazards, and restricted areas.
Manual production is repetitive and labor intensive. It can take more than 24 hours to complete work that AI-assisted automation can accomplish in seconds or minutes.
By accelerating these routine production tasks, AI enables analysts to focus on quality assurance, validation, and mission readiness instead of repetitive data entry.
How accurate is AI-generated geospatial data?
Operational decisions depend on trusted data.
During development of RAVe, Leidos found that automation not only accelerated production but also improved consistency and first-time quality. Analysts reviewing AI-generated outputs were unable to identify errors in the resulting products, demonstrating that automation can improve both speed and precision when paired with human oversight.
Automation reduces repetitive production errors while giving analysts more time to validate mission-critical information.
Will AI replace geospatial analysts?
Short answer: no.
AI excels at processing large volumes of data and automating repetitive work, but it cannot replace human judgment, operational experience, or mission context.
Analysts remain responsible for:
- Validating AI-generated outputs
- Interpreting operational context
- Assessing confidence and risk
- Applying mission expertise
- Delivering trusted intelligence to decision-makers
The future of geospatial intelligence isn't human versus machine. In reality, it's human-machine teaming, where AI accelerates production and analysts provide the expertise that builds trust.
How does AI improve maritime navigation?
Maritime safety depends on accurate, current geospatial data.
Nautical charts contain thousands of critical data points, including shipping channels, dredged areas, restricted waters, depth information, and submerged hazards. Missing or inaccurate information can have serious operational consequences.
By accelerating chart production while maintaining data quality, AI helps organizations deliver more current navigation products, supporting safer operations for the U.S. Navy and other maritime users.
How is Leidos advancing AI in geospatial intelligence?
Leidos is developing AI-enabled capabilities that help defense and intelligence organizations modernize geospatial production.
Technologies such as RAVe automate repetitive production tasks while keeping analysts at the center of every decision. This approach enables organizations to process more data, improve production efficiency, and deliver higher-quality geospatial products without sacrificing trust or human oversight.
The result is faster production, improved consistency, and better-informed decisions across defense, intelligence, and maritime missions.
RAVe by the numbers
📉 ~50% reduction in touch labor — work that was unattainable through manual production
✓ Over “four nines” (99.99%) accuracy, first-time-right, using automation
⏱ 24+ hours → seconds or minutes
Key takeaways
- AI helps analysts process geospatial data faster while keeping people at the center of mission-critical decisions.
- RAVe accelerates chart production, improving both speed and data quality through AI-assisted automation.
- Human-machine teaming delivers faster, more trusted intelligence for defense, intelligence, and maritime missions.
LEARN MORE ABOUT HOW AI IS TRANSFORMING GEOSPATIAL INTELLIGENCE
FAQ
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AI rapidly processes large volumes of imagery and geospatial data, allowing analysts to spend less time on manual production and more time evaluating information, identifying operational significance, and supporting decision-makers.
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AI can identify patterns and automate repetitive work, but it cannot understand mission context or make operational judgments. Human analysts ensure AI-generated outputs are accurate, explainable, and appropriate for mission use.
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AI reduces the time required to produce and update nautical charts, improves production consistency, and helps analysts deliver trusted navigation data more efficiently, supporting safer maritime operations.
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During development of RAVe, work that traditionally required more than 24 hours could be completed in seconds or minutes through AI-assisted automation. Leidos also achieved a 50% reduction in touch labor while maintaining high levels of data quality and first-time accuracy.
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Government agencies work with a range of organizations to develop AI-enabled geospatial capabilities. Leidos develops AI solutions that help modernize geospatial production, automate chart generation, and support trusted intelligence workflows across defense, intelligence, and maritime missions.
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Leidos developed RAVe (Raster Automation to Vector) to automate the conversion of raster maps and charts into structured geospatial vector data. The capability helps accelerate chart production while keeping analysts responsible for validating and delivering trusted geospatial products.
