# Urban Road Anomaly Monitoring Using Vision-Language Models for Enhanced Safety Management
## 1 Introduction  
Urban road anomalies, such as cracks and holes in road surfaces, garbage accumulation, traffic congestion, floods, fallen trees, fires, and traffic accidents, pose significant risks to public safety and urban infrastructure worldwide. According to the Global Status Report on Road Safety 2023, approximately 1.19 million people worldwide lost their lives due to road traffic accidents in 2021 [1]. In China, road traffic accidents continue to be a major public safety concern. In 2023, 255,000 road traffic accidents were recorded, resulting in direct economic losses amounting to 1.18 billion yuan [2]. Road anomalies are significant contributors to accidents, substantially affecting the safety and comfort of both drivers and passengers. This situation has prompted governments to invest heavily in maintenance [2, 3]. These statistics underscore the urgent need for enhanced road safety measures to mitigate the risks associated with urban road anomalies [4, 5]. Addressing these challenges necessitates timely road anomalies monitoring [6] and interventions [7]. The real-time system would provide early warnings [8], enabling timely interventions to prevent accidentsand reduce economic costs [9], thereby creating safer, more resilient urban environments and optimizing infrastructure management [10]. Monitoring road anomalies in real-time employs a variety of foundational techniques, each presenting its own characteristics. Conventional monitoring approaches frequently struggle to effectively evaluate risks and can be influenced by weather factors, leading to possible underestimations and insufficient responses [11; 12]. While ground-based monitoring systems are capable of collecting high-resolution, instantaneous data, they are associated with significant installation and maintenance costs and have limited coverage [13]. Remote sensing technologies enable efficient information gathering about Earth's surface features and infrastructure without the necessity of direct contact, supporting disaster evaluation and prompt relief efforts; however, their effectiveness may be compromised by adverse weather conditions [14; 15; 16]. Social media serves as a real-time information source but requires careful screening and processing of the data available therein [17; 18]. Recent studies have shown that advancements in artificial intelligence (AI) have greatly enhanced the capabilities of monitoring road anomalies [19]. Machine learning techniques, including K-Nearest Neighbors (KNNs) and Decision Trees (DTs), have been utilized in multiple investigations to minimize the need for human input and reduce the time necessary for mapping road anomalies [20; 21; 22]. An asphalt pavement pothole detection and segmentation method employs a wavelet energy field to emphasize pothole regions and achieves an overall accuracy of nearly 87%. However, the overall process was time-consuming and involved complex processing [23]. A monitoring system for road conditions was created in two stages to identify distressed areas. In the second stage, a single-layer artificial neural network (ANN) was trained [24]. For road waterlogging, an investigation designed an algorithm for flood mapping that integrated the Normalized Difference Water Index (NDWI) with a supervised classifier, achieving an overall accuracy of above 70% for different contexts [25]. Another research study illustrates a deep neural network methodology combined with Canny edge detection to identify submerged stop signs and assess floodwater depth [26]. These analyses indicate that hybrid or ensemble models tend to produce higher accuracy compared to standalone models, suggesting that the potential for developing highly accurate automated road conditions mapping tools is yet to be thoroughly investigated [27; 28]. Current studies have demonstrated feasible methods; however, there are notable gaps in data volume, accuracy, and real-time performance.