Using AI and Change Detection in Geospatial UAS Airfield Pavement Inspection for Pavement Management

The authors have conducted detailed pavement inspections at over 100 airports using uncrewed aerial systems with high resolution photogrammetry to identify pavement distresses and calculate pavement condition index using the formula of ASTM D-5340. New aerial vehicles with new 100-megapixel cameras allow the team to collect the same 1.5 mm pixel GSD imagery three times faster than the previously reported data collection system. The team uses artificial intelligence coupled with deep machine learning models to automatically identify both distress type and distress severity levels. The large library of distresses from 140 airports that have previously been flown and verified has greatly improved the accuracy of the distress identity and distress severity determination. Although the AI automatically generated distress determinations are reviewed by technicians, the quality of the AI generated distresses and severities have greatly improved from what we reported two years ago. The result is that distresses that may have gone unnoticed with a visual scan are being discovered and correctly identified by the AI. Although an FAA report has claimed that the UAS technology is not progressed enough to identify several low severity distresses (alkali-silica reaction, corner spalling, joint spalling, joint seal damage, depression, raveling, swell and weathering), the authors’ analyses using AI and deep machine learning have identified each of these low severity distresses without additional imagery. Now that the team is scanning many airports for a second time, we are using change detection at the pixel level to see changes that are happening to distresses and distress severities. Using AI and change detection software together has yielded important results. It provides the ability to visually see in what geospatial area specific distresses are increasing. A change of 1−3 PCI points is normally expected in year-to-year inspections. However, determining what types of distress changes are taking place is much more meaningful to the pavement management program (PMP).