A recent article in MIT News highlighted how a United States (U.S.) Air Force engineers developed AI-driven, drone-based systems that use deep learning (DL) and hyperspectral imaging to identify airfield damage and locate unexploded munitions.
The research centered on automating remote airfield assessments to minimize risks to personnel and improve detection accuracy. Drawing from real-world military operations and humanitarian demining efforts, Pietersen’s work showcased how AI and advanced imaging can enhance threat detection capabilities.
After graduation, the plan was to deploy these technologies in Guam, replacing manual inspections with autonomous, drone-based solutions.
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Related Work
Previous efforts relied on manual airfield damage assessments, with engineers conducting slow, high-risk inspections on foot. Attempts to automate the process often ran into difficulties—particularly when it came to detecting unexploded munitions, as traditional imaging methods struggled to distinguish them from surrounding debris.
Earlier research explored the use of hyperspectral imaging and deep learning (DL) for threat detection, but accurately separating unexploded ordnance from background clutter remained a challenge.
Still, these advancements laid important groundwork for drone-based AI systems, ultimately enhancing both safety and efficiency in airfield evaluations.
AI-Driven Remote Sensing
This research leverages drone-based systems powered by artificial intelligence (AI) and deep learning (DL) to improve remote airfield assessments. Traditional methods rely heavily on manual inspections—an approach that is not only time-consuming but also dangerous, particularly when identifying unexploded ordnance that often blends in with surrounding debris.
To overcome these limitations, the study explores computer vision techniques and neural networks to enhance object detection accuracy, addressing the constraints of ultra-high-resolution cameras.
A key focus is training DL models on diverse datasets to develop algorithms capable of distinguishing threats from natural terrain, enabling automated, real-time analysis. Hyperspectral imaging plays a critical role in this process.
By capturing electromagnetic radiation across a wide range of wavelengths it allows for the precise differentiation of objects—such as rocks versus unexploded munitions—that might appear visually similar in standard imagery. When combined with DL models, this data significantly improves object classification, making remote sensing a practical and safer alternative to manual assessments.
The integration of small, uncrewed aerial systems (sUAS) equipped with advanced imaging sensors has enabled the development of autonomous drones capable of surveying large areas quickly.
These drones feed collected data into machine-learning pipelines that analyze structural integrity and detect potential hazards. The research also explores optimized sensor fusion techniques—blending data from thermal, hyperspectral, and LiDAR imaging—to boost detection accuracy across varied conditions.
This transition from manual, on-foot operations to fully autonomous aerial inspections marks a significant step forward. And while the immediate applications focus on military airfield assessments, the broader potential is equally compelling.
In post-conflict zones contaminated with landmines and unexploded ordnance, this technology could support humanitarian missions. Collaborations with organizations such as the HALO Trust underscore how AI-driven remote sensing may accelerate mine clearance and enhance civilian safety.
As this PhD research approaches completion, the goal remains clear: to advance AI-powered drone systems that not only strengthen military infrastructure assessments but also contribute meaningfully to global demining efforts and infrastructure resilience.
Autonomous Airfield Monitoring
Pietersen's research showed that AI-driven drone systems using DL and hyperspectral imaging improved airfield damage assessments by enhancing accuracy and safety. This approach minimized risks for personnel, reduced false positives, and enabled automated detection of unexploded ordnances.
A significant finding was the effectiveness of sensor fusion in improving detection reliability. While hyperspectral imaging provided superior material differentiation, combining it with complementary sensing techniques such as thermal imaging and LiDAR enhanced AI model performance.
LiDAR contributed precise depth and structural data, while thermal imaging detected hidden threats based on temperature variations. The research demonstrated that DL models trained on multisensory data created a more robust detection system capable of identifying anomalies in diverse environmental conditions. Additionally, transfer learning played a crucial role in adapting AI models to new terrains with minimal retraining, ensuring rapid model updates for real-time airfield assessments.
The practical implementation involved deploying uncrewed aerial systems with AI-powered imaging sensors to navigate airfields and autonomously capture high-resolution multispectral and hyperspectral data.
The processed data allowed AI models to accurately map runway damage, classify debris, and detect potential hazards without human intervention. The study highlighted the potential of AI-driven drones to operate in post-attack or disaster scenarios, where manual inspections would be too dangerous or time-consuming. Automation reduced the operational burden on engineers and facilitated quicker decision-making, leading to faster response times in mission-critical environments.
Beyond military applications, the research had broader implications for humanitarian and civilian use. AI-powered remote sensing showed promise in post-conflict demining operations, aiding organizations like the HALO Trust in detecting unexploded ordnances in war-torn regions.
Furthermore, hyperspectral imaging and AI-driven aerial assessments could be applied to infrastructure monitoring, agriculture, and disaster response, enabling rapid analysis of structural integrity, land contamination, and environmental changes.
The discussion emphasized the necessity of continued advancements in AI model generalization, improved data labeling techniques, and enhanced hardware capabilities to refine detection accuracy and expand operational scalability.
Conclusion
To sum up, the research highlighted the potential of AI-driven drone systems using DL to enhance remote sensing for airfield damage assessments.
The study improved accuracy, safety, and efficiency in detecting unexploded ordnances by integrating hyperspectral imaging and advanced object detection.
The findings demonstrated how autonomous aerial assessments could replace manual inspections, enabling faster and more reliable recovery operations.
Journal Reference
Zach. (2025, March). Making airfield assessments automatic, remote, and safe. MIT News | Massachusetts Institute of Technology. https://news.mit.edu/2025/randall-pietersen-making-airfield-assessments-automatic-remote-safe-0313
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