In urban pavement crack detection, the single detection platform with visible light sensors is mostly used for detection. Visible light sensors are highly susceptible to lighting conditions. In low-light environments or areas with shadows over the cracks, this may result in false positives or missed detections of pavement cracks. The paper utilizes an image acquisition system that integrates multiple detection platforms, including visible light and infrared sensors, to enhance both the efficiency and accuracy of pavement crack detection. We propose an automatic pavement crack detection approach that uses the similarities and differences between visible light images and infrared image features (MSFSD) to achieve all-weather, real-time detection of pavement cracks. MSFSD mainly includes multi-source image fusion and pixel-level crack detection. In multi-source image fusion, a generative adversarial network (C-GAN) with skip connections and dilated convolutional blocks (CRes2Net blocks) is constructed. The structural similarity index (SSIM) function and the sum of the correlations of differences (SCD) function are used as the loss function of the generator, allowing the fusion image to retain more details and edge information in the source image. In pixel-level crack detection, a crack detection model is constructed, and CRes2Net is used as the core network to obtain pixel-level labels and perform semantic segmentation. It is verified by experiments that MSFSD achieves 85.1% precision and 88.4% recall in different types of crack detection. The MIoU and detection efficiency of MSFSD are 9% and 48% higher than other methods, respectively.

Quickly and accurately identifying water leakage is one of the important components of the health monitoring of subway tunnels. A mobile vision measurement system consisting of several high-resolution, industrial, charge-coupled device (CCD) cameras is placed on trains to implement structural health monitoring in tunnels. Through the image processing technology proposed in this paper, water leakage areas in subway tunnels can be found and repaired in real time. A lightweight automatic segmentation approach to water leakage using hybrid-supervised-deep-learning technology is proposed. This approach consists of the weakly supervised learning Water Leakage-CAM and fully supervised learning WRDeepLabV3+. The Water Leakage-CAM is used for the automatic labeling of data. The WRDeepLabV3+ is used for the accurate identification of water leakage areas in subway tunnels. Compared with other end-to-end semantic segmentation networks, the hybrid-supervised learning approach can more completely segment the water leakage region when dealing with water leakage in complex environments. The hybrid-supervised-deep-learning approach proposed in this paper achieves the highest MIoU of 82.8% on the experimental dataset, which is 6.4% higher than the second. The efficiency is also 25% higher than the second and significantly outperforms other end-to-end deep learning approaches.

High-speed railways are usually distributed in belts, which are characterized by long spans and uneven geological distribution. When uneven settlement occurs along the high-speed railway, it seriously affects the safe operation of the high-speed railway.^1,2 In the safety of train operation, the settlement time series data along the high-speed railway can provide important data and technical support for mastering the train operation status. To obtain settlement time series data of high-precision and high spatio-temporal resolution along high-speed railways, this paper deeply integrates Global Navigation Satellite System (GNSS) and Interferometric Synthetic-Aperture Radar (InSAR) data.^3,4

Burned area mapping based on remote sensing images is a key task in wildfire monitoring and holds significant importance for disaster assessment. In recent years, deep learning (DL) methods have made significant progress in this task and are gradually becoming the mainstream approach. However, the issue of existing training sample scarcity severely limits the accuracy and generalization of DL-based burned area mapping methods. To address this issue, we propose a controllable diffusion model, namely Wildfire-Diff, for synchronized generation of paired wildfire images and pixel-level masks. Different from current conditional diffusion models, we strengthen the realness of generated samples by constraining the spatial distribution and background of wildfires. Specifically, we design a vegetation-constrained wildfire mask generation network that can restrict burned areas to vegetation-covered regions, thereby reducing unrealistic fire scenes (e.g. wildfire on the river). Furthermore, we develop a background-aware wildfire image generation network to reduce the randomness of generated image background and mitigate the distortion issue. Benefiting from these two frameworks, our Wildfire-Diff can generate high-quality wildfire change detection samples to promote burned area mapping. Experimental results demonstrate the superiority of our Wildfire-Diff (30.42 FID) compared to other advanced diffusion models (e.g. ControlNet). Meanwhile, in downstream burned area mapping tasks, applying wildfire samples generated by our Wildfire-Diff can significantly improve the accuracy (IoU) of four change detection models (i.e., BIT, CGNet, EfficientCD and BAMCD) by at least 20%, which further reveals its great potential to address training sample scarcity and promotes downstream applications.