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Accurate analysis of traffic flow (TF) data is crucial for the vehicular applications. Conventional deep learning models require task-specific training and are susceptible to high-frequency disturbances, degrading the feature representation capability. To overcome these limitations, this paper proposes a Token-based Self-Supervised Network (TSSN) that can learn TF features in both tokenization and task-agnostic manners. It provides a properly bootstrapped pre-training model for various downstream tasks. In support of the edge computing and vehicular cloud computing, the pooled computational resources facilitate real-time inferences of downstream models. In TSSN, TF data are segmented into tokens. A pretext task, named as Masked Token Prediction (MTP), is then developed to allow TSSN to understand the underlying correlations of TF by predicting randomly masked tokens. By utilizing MTP, TSSN is able to extract the high-level intrinsic semantics of TF, and provide general-purpose token embeddings, leading to improved overall performance and enhanced ability to adapt to different tasks. By substituting the last fully-connected layers with a group of untrained new layers and fine-tuning using small-scale task-specific data, TSSN can be utilized for a variety of downstream tasks in vehicular applications. Simulation results indicate that the TSSN enhances overall performance in comparison to state-of-the-art models.

With a plethora of remote sensing (RS) images, deep neural network-based semantic segmentation model (SegModel) achieves commendable road extraction performance. However, the occlusions caused by vehicles, roadside objects, and shadows cannot be directly identified as road pixels, especially on high-resolution RS images. Therefore, relying only on a single SegModel to guarantee road connectivity and boundary smoothness in road extraction tasks is extremely difficult. To address this issue, this article puts forward a “segmentation-with-reconstruction” framework, which comprises a SegModel to generate the binary road labels from RS images and a reconstruction model to refine the road labels. Specifically, the former can be compatible with arbitrary existing SegModels, while the latter is built by our proposed model named all-visible denoising autoencoder (AV-DAE). The AV-DAE is designed to be an encoder–decoder architecture that takes topology-corruption road labels as inputs and true road labels as outputs. To better train the AV-DAE, we further present three noise-adding strategies to corrupt road labels for diverse patterns and train the AV-DAE to reconstruct them. Being RS-image-agnostic, the AV-DAE pays more attention to the spatial features rather than the spectral features, which enables it to recover the road topology by improving the connectivity and boundary smoothness. Finally, elaborate simulation results demonstrate that the proposed framework can significantly improve the connectivity and boundary smoothness of the extracted roads while achieving a competitive road extraction performance and high generalization ability compared to the benchmarks.

The geological characteristics of old landslides can provide crucial information for the task of landslide protection. However, detecting old landslides from high-resolution remote sensing images (HRSIs) is of great challenge due to their partially or strongly transformed morphology over a long time and thus the limited difference with their surroundings. Additionally, small-sized datasets can restrict in-depth learning. To address these challenges, this article proposes a new iterative classification and semantic segmentation network (ICSSN), which can significantly improve both object-level and pixel-level classification performance by iteratively upgrading the feature extraction module shared by the object classification and semantic segmentation networks. To improve the detection performance on small-sized datasets, object-level contrastive learning is employed in the object classification network featuring a siamese network to realize global features extraction, and a subobject-level contrastive learning (SOCL) method is designed in the semantic segmentation network to efficiently extract salient features from boundaries of landslides. An iterative training strategy is also proposed to fuse features in the semantic space, further improving both the object-level and pixel-level classification performances. The proposed ICSSN is evaluated on a real-world landslide dataset, and experimental results show that it greatly improves both the classification and segmentation accuracy of old landslides. For the semantic segmentation task, compared to the baseline, the F1 score increases from 0.5054 to 0.5448, the mean intersection over union (mIoU) improves from 0.6405 to 0.6610, the landslide IoU grows from 0.3381 to 0.3743, the pixel accuracy (PA) is improved from 0.945 to 0.949, and the object-level detection accuracy of old landslides surges from 0.55 to 0.90. For the object classification task, the F1 score increases from 0.8846 to 0.9230, and the accuracy score is up from 0.8375 to 0.8875.

Methanol oxidation reaction (MOR) in anodes is one of the significant aspects of direct methanol fuel cells (DMFCs), which also plays a critical role in achieving a carbon-neutral economy. Designing and developing efficient, cost-effective, and durable non-Pt group metal-based methanol oxidation catalysts are highly desired, but a gap still remains. Herein, we report well-defined hierarchical NiZnx@CuO nanoarray architectures as active electrocatalysts for MOR, synthesized by combining thermal oxidation treatment and magnetron sputtering deposition through a brass mesh precursor. After systematically evaluating the electrocatalytic performance of NiZnx@CuO nanoarray catalysts with different preparation conditions, we found that the NiZn1000@CuO (thermally oxidized at 500 °C for 2 h, nominal thickness of the NiZn alloy film is 1000 nm) electrode delivers a high current density of 449.3 mA cm–2at 0.8 V for MOR in alkaline media as well as excellent operation stability (92% retention after 12 h). These outstanding MOR performances can be attributed to the hierarchical well-defined structure that can not only render abundant active sites and a synergistic effect to enhance the electrocatalytic activity but also can effectively facilitate mass and electron transport. More importantly, we found that partial Zn atoms could leach from the NiZn alloy, resulting in rough surface nanorods, which would further increase the specific surface area. These results indicate that the NiZn1000@CuO nanoarray architecture could be a promising Pt group metal alternative as an efficient, cost-effective, and durable anode catalyst for DMFCs.