## 1 Introduction  
Highway inventory plays a critical role in highway maintenance and asset management. State departments of transportation (DOTs) and local transportation agencies always need up-to-date inventory data to establish the condition of road networks, prioritize reconstruction and repair work, and evaluate highway assets [1]. Traffic signs, as important highway inventory, play an imperative role in road safety and efficiency by giving instructions or providing information to drivers or autonomous vehicles. Based on the sensing platform, existing highway inventory methods can be classified into two categories: ground-based and air- or space-based methods [1]. Ground-based methods include field inventory, photo/video log, integrated global positioning system (GPS)/global information system (GIS) mapping, terrestrial light detection and ranging (LiDAR), and mobile LiDAR. Air-based methods include aerial/satellite photography and airborne LiDAR. Each method has its advantages and limitations. Recent studies show that the total cost of aerial mapping is much lower than other methods considering the time and personnel needed for large-area inventories (e.g., a whole state highway inventory). As unmanned aerial vehicles (UAVs) become more easily accessible and inexpensive, it is likely that the aerial mapping methods for road inventory will become even less expensive and more efficient in the future. Here, we introduce several air-based traffic sign detection methods for different types of data captured by different types of sensors. Two common categories of these methods utilize images and light detection and ranging (LiDAR) data. Here, we briefly review several representative approaches that use camera images and LiDAR data. Several image-based methods have been introduced in recent years to detect and recognize traffic signs. For instance, [PERSON] et al. [2] introduce a multi-view constrained 3D reconstruction algorithm, which incorporates color information of traffic signs in imagery data and provides an optimum 3D silhouette for traffic sign detection. [PERSON] and [PERSON] [3] propose to train a support vector machine (SVM) classifier using histogram of oriented gradient (HOG) to detect traffic signs. [PERSON] et al. [4] extract traffic sign candidates by enhancing the red and blue channel of RGB images based on the assumption that most of the signs are available in these two colors. They further train a SVM-k-nearest neighbor classifier to extract traffic signs among the candidates. Despite the favorable performance of these image-based algorithms, visual features of traffic signs such as color, shape, and appearance are often sensitive to illumination conditions, angles of view, etc. Recently, researchers have proposed various methods utilizing LiDAR technology for traffic sign detection. However, the number of published work in this aspect is relatively small. Most of these methods use mobile light detection and ranging scanning (MLS) data since they usually have better quality and density than airborne LiDAR. As one of the pioneer works, [PERSON] et al. [5] initially classify the data points to three major categories such as ground surface, objects located on the ground, and objects off the ground. They further incorporate geometrical features including size, shape, and orientation to extract traffic signs from on-ground points. [PERSON] et al. [6] propose to utilize principal component analysis (PCA) to distinguish pole like objects from planar ones in MLS data. In addition, they classify pole like objects into three classes, namely, utility poles, lamp posts, and street signs. [PERSON] and [PERSON] [7] eliminate ground points using a voxel-based upward growing method. They then cluster and segment off-ground points into individual objects via Euclidean distance clustering and voxel-based normalized cut segmentation. [PERSON] et al. [8] propose to find an optimized intensity threshold in order to segment points corresponding to traffic sign panels.