Aircraft identification in airport operations is critical to various applications, including airport planning and environmental studies. Previous research and commercially available systems heavily rely on recognizing aircraft tail numbers using text recognition. However, this approach alone does not provide accurate results in situations when the tail number visibility is reduced or obstructed. Furthermore, general aviation aircraft are harder to identify because they are small in size, and their tail numbers include substantial variations in fonts, sizes, and orientations. To tackle these issues, we propose a two-step computer vision-based aircraft identification method, first identifying the aircraft type and then recognizing the tail number in a probabilistic multi-frame-based (MFB) framework. In the first step, a convolutional neural network (CNN)-based aircraft classifier is customized to decrease the search space in the registration database. In the second step, the identification process is finalized by integrating the text recognition results into the designed probabilistic MFB framework. The proposed method achieves approximately 90% identification accuracy when tested on video data collected from three general aviation airports. This is a significant improvement compared to text recognition alone, which recognizes 67% of the individual tail number characters.INDEX TERMS Intelligent transportation systems, computer vision, airports, aircraft.

Highway asset condition is of the utmost importance for transportation maintenance and pedestrian safety. Transportation facility managers must have up-to-date information on the status of all transportation assets to keep the transportation facilities operating at their highest level. Because of the sheer volume of transportation assets, an efficient and affordable data-collection procedure is necessary to gather the as-is status of the assets and create an asset inventory. Some pioneer departments of transportation in the United States use mobile Light Detection and Ranging (LiDAR) to monitor highway assets and pavement condition data. Not only is the laser scanning equipment expensive, but the operator in charge of using the equipment must have special technical knowledge that may not be accessible to every individual. More recently, image-based reconstruction, known as photogrammetry, has emerged as a cheaper and simpler technology than LiDAR. Image-based 3D reconstruction can be done using a digital camera, such as a digital single-lens reflex camera or even a smartphone. This paper presents a full review of various research studies conducted on highway asset management and pavement condition assessment using spatial data modeling by the use of LiDAR and photogrammetry. This paper also presents two case studies to fill the current research gap in highway asset inventorying using photogrammetry. The results show the superiority of mobile LiDAR for highway asset inventorying and the possibility of having photogrammetry as a reliable alternative technology only in favorable illumination conditions.

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