## 1 Introduction  
Research on damage detection of road surfaces using artificial intelligence techniques has seen an increased interest in recent years [1; 2; 3; 4], particularly with the arrival of new digital transformation paradigms (i.e., smart cities). Road maintenance efforts and follow ups had been difficult to automate in the past, and yet, the problem is of paramount importance, as demonstrated by the widespread push and adoption of protocols and standards in many countries to tackle these issues. Nonetheless, the automated inspection and follow up of road damages remains a complex problem, as most efforts do not consider effective digitization strategies, and thus governments and agencies lack mechanisms to maintain accurate and up-to-date databases of the road conditions. Another issue is the lack of experts that can assess the state and spread of several structural damages, as oftentimes, the evaluation can be very subjective. To make matters worse, traditional methods for collecting samples in the field are time-consuming and cost-intensive. Therefore, several research and commercial efforts have been conducted to aid government agencies to automate the road inspection and sample collection process, making use of technologies with varied degrees of complexity [5]. Initial approaches made use of mobile measurement system (MMS), which combined various sensors (i.e., inertial profilers, scanners), in tandem with sophisticated imaging techniques for automating the data collection process (although not necessarily identifying particular types of damages in real time). The information gathered by MMS is usually fed to artificial intelligence methods as georeferenced information to produce a \"digital twin\" representation of the collected data, fostering big data approaches for road damage prevention and prognosis. Although these approaches have shown promising results [6], many of them are targeted by design to specific types of damages (i.e., cracks) and made use of rather small datasets. In many instances, this was because of the lack of large and diverse datasets and to sub-optimal feature extraction and machine learning algorithms, leading to solutions that did not perform well in very complex scenarios (i.e., for images under varying degrees of illumination, camera perspective, among others). Nonetheless, recent efforts in the domain have seen a surge in performance owing to the adoption of deep learning-based computer vision acquisition systems. Such artificial intelligence-based systems are in general less costly than other technological choices and, if implemented properly, they can represent a cost-effective solution for agencies or governments with low budgets. Furthermore, these AI-based approaches can be augmented if they are coupled with mobile and cloud computing components; the former can be exploited for implementing lightweight mobile acquisition systems, while the latter can be used for storing the information captured and processed by these edge sensors for carrying out further big data analyses. For instance, as the captured information is geo-localized and digitized, the road damages can be tracked over time if implemented within an asset management tool for data analytics (i.e., planning, allocation of budgetary resources, among others). This approach suits particularly well with modern digital transformation paradigms and can be readily deployed as business model and a means for improving governmental decisions. For instance, the United Kingdom launched the Digital Roads Challenge to attain this goal. In this article, an initial step towards a road damage monitoring and management tool is introduced. The research presented herein is an extension of previous work in road damage detection; in this article, we introduce and make use of a new and wider-reaching dataset, consisting of various types of asphalt damages (see Figure 1: longitudinal cracks, alligator cracks, potholes, bumps, patches). To the best of our knowledge, this database is one of the most complete in the recent literature.