Assessing pavement condition is essential in any efforts to reduce future economic losses and improve the pavement performance. The resulting data are used as a record to evaluate pavement performance and assess their functionality and reliability. Traditional pavement condition assessment approaches rely on expert visual inspection and observational information along with testing using specialized equipment. However, these approaches are challenging because of the cost associated with assessment, safety issues, and the accessibility restrictions, especially after natural hazard events. This paper aims to develop an automated classification model to rapidly assess pavement condition by classifying pavement distresses using image classification that is based on Convolutional Neural Network (CNN) model. High-resolution aerial images representing alligator and longitudinal cracks for flexible pavements are collected using Unmanned Aerial Vehicle (UAV) images. The results of the developed model indicate an accuracy of 96.7% in classifying the two categories of pavement distress, while the use of UAV provides flexibility and manoeuvrability to capture the necessary data without risking personal safety and provides operational benefits in relatively lesser time. The methodology behind the developed model will help to reduce the need for on-site presence, increase safety, and assist emergency response managers in deciding the safest route to take after hurricane events. Additionally, application of the model will enable pavement engineers in rapidly assessing the pavement damage, aid in making quick decisions for road rehabilitation and recovery, and devise a restoration or repair plan.