Satellite Eyes on Runway Safety: Predicting Airport Pavement Friction Using Convolutional Neural Networks and Image Processing

Abstract

Runway safety in landing and takeoff operations is strongly influenced by skid resistance, which may be reduced by rubber buildup from aircraft braking. Conventional friction measurement methods require specialized equipment and operational interruptions, limiting their frequency and increasing costs. This study explores the use of satellite imagery and convolutional neural networks (CNNs) as a non-intrusive and cost-effective alternative for classifying runway friction coefficients. A dataset of about 7,000 Google Earth images of Brazilian aerodrome runways was matched with measured friction coefficients. Three preprocessing techniques (contrast limited adaptive histogram equalization [CLAHE], Gaussian filter, and wavelet) and five CNN architectures (simple CNN, ResNet50, DenseNet121, InceptionV3, and VGG16) were evaluated. The best performance was obtained with CLAHE + Gaussian preprocessing and DenseNet121, achieving over 74% accuracy in cross-validation. External validation with unseen satellite images confirmed robustness, while tests with unmanned aerial vehicle imagery showed limited generalization across visual domains. The results indicate that deep learning combined with remote sensing can support airport pavement management. The proposed approach complements direct friction monitoring, offering a scalable, low-cost, and non-intrusive tool to identify critical areas, guide preventive maintenance, and enhance operational safety.