# Urban Road Anomaly Monitoring Using Vision-Language Models for Enhanced Safety Management
## 2 Methods
### Data Collection and Annotation  
The initial stage involves gathering and filtering visual data suitable for training and testing. We sourced over 18,000 Urban Road Anomaly images from open databases, search engines, and video screenshots, and self-shoot photos, ensuring that they met the necessary size and quality requirements for training. During the image search process, we focused on images depicting various road anomalies in urban areas, mainly the various road anomalies--such as bad roads, fallen trees, fires, floods, garbage, traffic accidents, and jams--that threaten road safety and traffic efficiency. Additionally, we considered different environmental conditions, including daytime and nighttime, as well as sunny and rainy weather, to enhance the urban road dataset. After data cleaning, 15,223 images remained as the original dataset. Among them, approximately 82% were labeled as daytime, while about 18% were labeled as nighttime. Around 77% of the data were labeled under sunny or cloudy conditions, approximately 18% during rainy or windy weather, and 5% during freezing or snowy conditions. The annotation process utilized a dual verification approach that combined human and machine collaboration. The InternVL2.5-MPO [43] model and a human annotated the images, with a second human annotator reviewed for final confirmation. The dataset was divided into 80% for training and 20% for testing and validation. We annotated the image data according to the following three steps: first, the eight types of road data were classified (Table 1). Initially, rules to indicate urban road anomalies were established. Our focus was exclusively on road safety; non-road safety events was not included in our analysis. Next, for the road flooding data, we delve deeper into the assessment of waterlogging depths on roadways. The depth of floodwater plays a critical role in flood mapping [9; 10]. Accurately understanding water depth is essential for assessing flood impacts and developing strategies to mitigate flood risks [44]. To classify the levels of road flooding based on depth, we referenced the Beijing Urban Waterlogging Risk Map Report published by \begin{table}
\begin{tabular}{c c c} \hline \hline
**No.** & **Types** & **Description** \\ \hline
1 & Bad road & The road exhibits significant cracks and potholes, posing potential hazards to vehicle safety and traffic flow. \\
2 & Fallen tree & There are fallen tree obstructs the roadway, creating a safety hazard for vehicles. \\
3 & Fire & A fire is present on the road, and poses a risk to nearby traffic. \\
4 & Flooding & The road is subject to varying levels of flooding, which may impede vehicle traffic flow. \\
5 & Garbage & Accumulated garbage is evident on the road, presenting obstacles and safety risks to traffic. \\
6 & Normal & The road appears in good condition with no noticeable hazards, facilitating normal traffic flow. \\
7 & Traffic accident & A traffic accident has occurred on the road, potentially disrupting traffic and posing safety concerns. \\
8 & Traffic jam & A traffic accident has occurred on the road, potentially disrupting traffic and posing safety concerns. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The urban road anomaly annotations. the Beijing Water Authority [45]. Specifically, water depths measuring less than 10 mm are deemed to have an almost negligible impact. Such shallow depths present minimal risks to both traffic flow and infrastructure. Depths ranging from 10 mm to 150 mm are classified as low-risk, whereas those equal to or exceeding 150 mm are classified as waterlogging, which presents considerable safety hazards [46; 47]. This classification provides a practical framework for evaluating flood severity and road safety. To further evaluate the impact of these road issues on traffic, we classified them according to their safety levels (Table 2). The road safety classification employs three categories--GREEN, LIGHT GREEN, and YELLOW--to effectively monitor and manage road conditions in real-time.