Enterprise AI Analysis
Methods for GIS-Driven Airspace Management: Integrating Unmanned Aircraft Systems (UASs), Advanced Air Mobility (AAM), and Crewed Aircraft in the NAS
Authors: Ryan P. Case and Joseph P. Hupy
Publication: Drones 2026, 10, 82, MDPI
This research presents a GIS-driven framework for modernizing airspace management by integrating diverse aviation data, addressing critical safety and efficiency challenges in the National Airspace System (NAS).
Executive Summary
The rapid growth of Unmanned Aircraft Systems (UASs) and Advanced Air Mobility (AAM) presents significant integration and safety challenges for the National Airspace System (NAS), often relying on disconnected Air Traffic Management (ATM) and Unmanned Aircraft System Traffic Management (UTM) practices that contribute to airspace incidents. This study evaluates Geographic Information Systems (GISs) as a unified, data-driven framework to enhance shared airspace safety and efficiency. A comprehensive, multi-phase methodology was developed using GIS (specifically Esri ArcGIS Pro) to integrate heterogeneous aviation data, including FAA aeronautical data, Automatic Dependent Surveillance-Broadcast (ADS-B) for crewed aircraft, and UAS Flight Records, necessitating detailed spatial–temporal data preprocessing for harmonization. The effectiveness of this GIS-based approach was demonstrated through a case study analyzing a critical interaction between a University UAS (Da-Jiang Innovations (DJI) M300) and a crewed Piper PA-28-181 near Purdue University Airport (KLAF). The resulting two-dimensional (2D) and three-dimensional (3D) models successfully enabled the visualization, quantitative measurement, and analysis of aircraft trajectories, confirming a minimum separation of approximately 459 feet laterally and 339 feet vertically. The findings confirm that a GIS offers a centralized, scalable platform for collating, analyzing, modeling, and visualizing air traffic operations, directly addressing ATM/UTM integration deficiencies. This GIS framework, especially when combined with advancements in sensor technologies and Artificial Intelligence (AI) for anomaly detection, is critical for modernizing NAS oversight, improving situational awareness, and establishing a foundation for real-time risk prediction and dynamic airspace management.
Key Outcomes & Impact
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Reduction in Airspace Incidents
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Improvement in Situational Awareness
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Faster Anomaly Detection
Deep Analysis & Enterprise Applications
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Critical Separation Confirmed
459ftMinimum Lateral Separation Achieved (UAS vs. Crewed Aircraft)
Unified Airspace Data Processing Flow
Data Ingestion & Harmonization
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Spatiotemporal Alignment (4D CRS)
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Visualization & Analytical Subsystem
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Direct Measurement & Pattern-of-Life Analysis
| Aspect | Traditional ATM/UTM | GIS-Driven Framework |
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| Data Integration |
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| Situational Awareness |
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| Conflict Analysis |
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| Scalability & Modernization |
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Purdue University Airspace Incident: DJI M300 & Piper PA-28-181
On October 18, 2023, a university-operated DJI M300 UAS conducting a mapping mission near Purdue University Airport (KLAF) encountered a Purdue University Piper PA-28-181 Archer TX (callsign 'PDU57'). The UAS operator, who had LAANC approval and was monitoring KLAF Tower communications, initiated an avoidance maneuver upon spotting the aircraft. Post-flight GIS analysis confirmed a near-miss interaction with a minimum lateral separation of 459 feet and vertical separation of 339 feet.
- UAS Activity: DJI M300 performing an RGB mapping mission (lawnmower pattern) at 1047 FT altitude (350 FT AGL) with RTK positioning enabled. Approved via LAANC waiver.
- Crewed Aircraft: Piper PA-28-181 Archer TX (PDU57) performing pilot training, including extended circles to land on Runway 23 at KLAF. ADS-B data provided GNSS-derived geoaltitude.
- Interaction Timeline: PDU57 entered KLAF Class D airspace around 5:02 PM. At 5:05:09 PM, the UAS operator observed PDU57 approaching and initiated a controlled descent to increase separation. Closest point of interaction was at 5:05:23 PM.
- Key Measurements: At the closest point, lateral separation was 458.94 FT (geodesic) and vertical separation was ~339 FT (based on reported AGL altitudes). PDU57 was at 1250 FT barometric altitude (~644 FT AGL), and DJI M300 was at 911 FT (213.9 FT AGL) after its descent.
- GIS Application: The GIS framework enabled precise spatiotemporal analysis, visualizing 2D maps and 3D scenes of the trajectories, confirming the incident, and quantifying separation distances, demonstrating its capability for real-world airspace safety oversight.
Figure 9. Analysis of DJI M300 RGB and PDU57 interaction (3D scene).
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Your AI Implementation Roadmap
A structured approach to integrate GIS-driven AI into your airspace management, ensuring safe and efficient operations.
Phase 1: Data Stream Integration & Harmonization
Establish real-time APIs for live ADS-B, UAS telemetry, and FAA aeronautical data. Develop automated ETL pipelines to standardize formats, resolve altitude discrepancies (AGL, MSL, FL), and synchronize temporal references across all datasets into a 4D (x,y,z,t) GIS model.
Phase 2: Advanced Spatial-Temporal Analytics
Implement GIS-based algorithms for dynamic 2D/3D trajectory modeling, conflict detection (spatial separation, time-to-collision), and 'Pattern-of-Life' analysis. Develop predictive models for congestion and potential near-miss hotspots using historical data.
Phase 3: AI-Enhanced Risk Prediction & Advisory Systems
Integrate AI/ML models trained on 'Patterns-of-Life' to detect anomalous flight behavior (outliers) and non-participatory aircraft. Develop an advisory system for ATC, crewed aircraft, and UAS operators, generating tiered warnings based on calculated collision probabilities and real-time risk assessment.
Phase 4: Scalable Deployment & Regulatory Integration
Expand the framework to support multi-airport, BVLOS, and AAM operations. Collaborate with regulatory bodies (FAA) to inform NOTAMs, EFB updates, and dynamic airspace management policies, ensuring interoperability and compliance across the NAS.
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