## 1 Introduction  
Road surface anomalies, such as potholes, cracks, rutting, or speed bumps deterioration, result from the constant usage, traffic loads, weather conditions, and age of the infrastructure and materials used in the construction of the roads [1, 2]. These anomalies can be referred to as any deviation or variation from standard road conditions [3]. Furthermore, road defects have financial costs for governments to constantly maintain the road and keep it in good condition [4]. Moreover, it is crucial to attend to and monitor the road pavement condition due to the potential harm or accidents that could inflict on the vehicle users and pedestrians, its impact on fuel consumption, and the potential vehicle damage that these irregularities could inflict [5]. In addition, according to the World Bank, the density of paved roads in an optimal state can be used as an indicator of the economic strength and competitiveness of a country [6, 7]. These factors make monitoring and maintaining the road in an optimal condition a crucial task for governments [8]. The traditional approach to monitoring and maintaining the road's optimal condition is to employ Pavement Condition Index (PCI) surveys that are based on human observations. These surveys have been used by international road and highway technicians as a reference to diagnose road anomalies [9]. The roughness of the road surface is another crucial indicator used to assess the quality of roads and detect cracks and bumps [10, 11]. However, in the case of PCI surveys, they are prone to subjective evaluation by the technician and can put the health of road operators at risk [12]. Otherwise, visual inspection methods are time-consuming and prone to human errors [13]. Thus, to counter the disadvantages of traditional approaches to evaluating the condition of roads, the literature has proposed developing systems that can automatically detect and classify these defects. There is great interest in developing these systems due to the potential impact they could have on intelligent transportation systems [14] and advanced driver assistance systems [15]. The systems developed in the literature for road surface anomaly detection and classification can be divided into vision-based, vibration-based, and 3D-reconstruction-based techniques [16]. Vision-based techniques use images to determine the presence of road anomalies through image processing and deep learning algorithms. On the other hand, the vibration-based techniques mainly employed inertial sensor measurements to detect and classify the presence of road anomalies. Finally, 3D-based reconstruction methods use stereo-vision technology to recognize and characterize the presence of road surface defects. The main goal of road surface anomaly detection and classification systems is to generate robust platforms that can provide information on the quality of the roads and warn of potential hazards [17]. A summary of the advantages and disadvantages of these systems can be appreciated in Table 1. As can be seen, vibration-based techniques have the main advantage of being the most cost-effective of the three methods since data are usually collected from smartphones [18]. However, this system requires the driver to pass over the anomaly to detect and characterize it since it relies on the measurements of inertial sensors such as gyroscopes and accelerometers. In the case of vision-based methods, they can detect the anomaly without passing over the anomaly. However, it is susceptible to lightning and shadow conditions. Finally, 3D reconstruction techniques can model the anomaly more precisely; nevertheless, it is a more expensive method [16]. Vibration-based techniques for road surface anomaly detection and classification have gained popularity due to the cost-effectiveness of this type of system. Inertial sensors (i.e., accelerometers and gyroscope sensors) commonly used for developing these systems can be easily embedded and used through smartphones [19].