With the rapid advancement of ecological civilization construction, prioritizing green stormwater infrastructure to address urban stormwater management issues has become an important strategy for ecological priority and green development in sustainable urban development. Green stormwater infrastructure, as a major facility in the construction of sponge cities, can reduce the generation and external discharge of runoff and play a purification role. However, there are various types of green stormwater infrastructure, each with different control effects and applicable conditions. Therefore, to facilitate the planning, design, acceptance, assessment, and monitoring evaluation of sponge city green stormwater infrastructure, this study proposes the “sponge equivalent” method. By comparing the control effects of different facilities with bioretention facilities, the method standardizes the effects, making them easier to understand and apply. Taking a typical area of Beijing and its urban roads as examples, the study analyzed and applied planning and design control strategies. The results show that for a residential area of 1 km², to achieve the annual runoff total control rate target of 85%, the method of converting runoff volume control equivalents, using bioretention pools as a benchmark, allows for the calculation of various combinations of areas of different types of green stormwater infrastructure, such as sunken green spaces, permeable paving bricks, green roofs, and water storage tanks. This optimizes the planning index of Beijing, which mandates stormwater detention facilities for new projects with a hardened surface area of 2000 m² or more. The sponge equivalent method can optimize the planning and design control strategy of green stormwater infrastructure, allowing for rapid assessment and application of the design scale of green stormwater infrastructure in areas during the planning and design stage, providing theoretical and technical support for ecological and green urban stormwater management. The application of this research method helps promote green development and ecological priority in urban sustainable development strategies, and the conclusions provide valuable references for decision-makers and practitioners in related fields.

Efficient detection and accurate three-dimensional characterization of road potholes are crucial for road maintenance and traffic safety. To address the issues of high cost and poor environmental adaptability in existing detection methods, this study proposes a lightweight pothole detection and 3D reconstruction method based on binocular stereo vision and deep learning. A ZED 2i binocular camera was used to build a vehicle-mounted acquisition system, combined with the Mask R-CNN model to achieve pothole detection and pixel-level segmentation. The 3D point cloud of potholes was reconstructed using the principles of binocular stereo vision, and a dynamic mesh density method was proposed to optimize surface area calculation. Additionally, the RANSAC algorithm was employed to fit the ground plane and extract depth parameters. Experimental results demonstrate that this method can achieve precise measurements of pothole depth and surface area at a speed of 40 km/h, with relative errors of 12.53% and 18.19%, respectively, and an average accuracy of 82% for damage ratio (DR) calculation. Furthermore, an MSRCP image enhancement technique and a sliding window cropping strategy (overlap rate of 0.7) were used to construct a dataset containing 6,416 images, significantly improving the model's robustness in complex scenarios such as shadows and varying lighting conditions. This study provides road maintenance departments with a low-cost, high-precision intelligent pothole detection solution, reducing hardware costs by 90% compared to traditional laser sensors, and demonstrates significant value for engineering applications.

To enhance the efficiency and visualization level of road maintenance work, this paper proposes a lane-level road defect visualization method based on multi-source data fusion. Traditional visualization methods usually only display single data or overall road conditions, which are difficult to meet the needs of intuitive presentation of complex road operation situations. To address this, this paper combines multi-source data such as Beidou GPS data, road inspection images, defect detection results, lane line information, and camera calibration to construct a complete multi-source data fusion visualization framework. Firstly, by introducing the Polar R-CNN network model to efficiently extract lane line information, and using the improved YOLOv8 model for object detection of road defects; secondly, in order to obtain the morphological features of road defects, this paper proposes an image segmentation method based on anchor box cropping and improved Otsu threshold algorithm, which effectively enhances the extraction effect of crack texture details; then, inverse perspective mapping (IPM) is used to transform the inclined images into orthographic images to achieve accurate mapping of the spatial positions of objects. The experimental results show that this method performs well in lane line detection, defect shape extraction, and spatial positioning, and can accurately visualize the display of different types of defects in multiple lanes, providing an intuitive and efficient decision support tool for road maintenance departments. The visualization scheme proposed in this paper not only enhances the interpretability and interactivity of data but also provides an important reference for the development of future intelligent road inspection systems.

With the continuous increase in the mileage of highway maintenance in our country, the tasks of highway maintenance are becoming increasingly heavy, posing new challenges for highway maintenance management. Current research on data models for highway maintenance mainly focuses on describing their spatiotemporal attributes, lacking a comprehensive expression of the characteristics and relationships of entities within highway maintenance scenarios. Moreover, traditional data management methods have many shortcomings in visualization and multidimensional information representation. Therefore, this paper constructs a multi-granularity spatio-temporal object data model framework aimed at intelligent highway maintenance by combining Multi-Granularity Spatio-Temporal Object Data Model (MGSTODM) with highway maintenance technical standards. An experiment was conducted using a highway maintenance scenario in a certain area of Beijing as an example. The results show that this model can efficiently express the multidimensional features of highway maintenance data, achieving a more comprehensive, realistic, and dynamic visualization of highway maintenance information, thus providing more effective support for maintenance data management and decision-making.