A Deterministic UAS-Based Workflow for PAPI Red–White Transition Detection and Angle Estimation with Theodolite Validation

Precision Approach Path Indicator (PAPI) systems are critical visual aids for supporting flight crews during the final approach phase by providing visual information on the aircraft’s vertical position relative to the desired glide path. Their correct operation and angular setting are therefore essential for maintaining operational safety at aerodromes. This study develops and field-evaluates a deterministic UAS-based workflow for automatic PAPI light localization, RED/WHITE/UNDEFINED state classification, red–white transition detection, and angular reconstruction from geotagged UAS observations. Data were acquired at two Colombian aerodromes, Perales Airport (SKIB) and Flaminio Suárez Camacho Aerodrome (SKGY), using a DJI Matrice 400 equipped with Zenmuse P1 and H30T optical payloads under manual vertical flight profiles and two illumination conditions. The proposed algorithm integrates luminance-based saliency extraction, four-light geometric validation, localized ROI-based chromatic classification, and RED → WHITE transition-event detection to estimate the transition angle of each PAPI unit from geotagged UAS observations. Results differed between the two evaluated site–payload configurations. In the SKGY–H30T configuration, the use of zoom and background-attenuation settings facilitated target isolation, and all 255 processed images were correctly classified during manual verification. In the SKIB–P1 configuration, 96.57% image-level classification accuracy was obtained, with the observed misclassifications mainly associated with city lights and luminous halos between adjacent PAPI units. Angular agreement with theodolite measurements also differed between the two datasets. Because each optical payload was evaluated at a different aerodrome, these differences cannot be attributed independently to payload characteristics; they represent the combined response of the payload, acquisition settings, background complexity, illumination, and site-specific conditions.