For the evaluation of traffic infrastructure, asphalt pavement aging conditions are crucial. Due to the complexity of identifying and monitoring asphalt pavement aging conditions, many current studies tend to use satellite remote sensing methods. We conducted an extraction experiment on the aging status of asphalt pavement using on-site measured Pavement Surface Condition Index data and Gaofen-2 satellite (GF-2) high-resolution remote sensing images based on comprehensive references to previous research results. Based on our experimental results, the difference health index, ratio health index, and normalized difference health index can reflect asphalt pavement aging to varying degrees, but the correlation is relatively weak. The purpose of this paper is to propose a new asphalt pavement aging index (PAI), namely the PAI, based on sufficient experimental analysis. In addition to identifying asphalt pavement aging perfectly, PAI has a good ability to discriminate between road interference information, such as shadows and vehicles, after it has been verified. There is a significant linear relationship between its correlation coefficient R and pavement surface condition index, which is 0.894. The evaluation results of three sets of ground verification points obtained by applying PAI also demonstrate its practicality. Therefore, the combination of PAI and GF-2 high-resolution remote sensing images can be used to evaluate the aging status of asphalt pavements.