Traffic flow prediction occupies a pivotal position in intelligent transportation systems, and accurate traffic flow prediction is of great significance for alleviating traffic congestion and reducing the incidence of traffic accidents. To improve the accuracy of traffic flow forecasts, it is necessary to consider the historical data over a longer period. However, most of the existing methods only consider part of the recent historical time information, ignoring the implied fluctuation of the traffic flow in some regions in the historical contemporaneous time interval. Therefore, we propose a multidimensional long-term spatio-temporal attention model for traffic flow forecasting by capturing time series correlations. In this model, we design a multi-temporal dimensional attention mechanism and a deep fusion extraction convolutional neural network to capture multidimensional temporal information and fuse spatio-temporal correlations to predict traffic flow. The experimental results on two real datasets show that the proposed model outperforms the compared models.

Road network extraction from remote sensing images is a fundamental task for applications like autonomous driving and urban planning. Mainstream methods, however, face a critical trade-off: segmentation-based approaches provide high geometric detail but often yield fragmented roadmaps, while graph-based approaches ensure connectivity but can sacrifice fine-grained accuracy. While hybrid models have been explored to resolve this, effectively fusing pixel-level features with structural information remains a key challenge. To address this, we propose PAB-Road, a novel framework with a unique fusion mechanism. Its core novelty is a multitask UNet that learns a patch-wise boundary representation to explicitly model local connectivity. This learned information then guides the synthesis of a geometrically accurate and structurally coherent road network. Experimental results in the real dataset show that PAB-Road achieves a compelling F1 score of 79.29%, demonstrating the effectiveness of our proposed fusion strategy.

Cloud mask underpins accurate precipitation nowcasting, which in turn is vital for understanding the hydrological cycle, supporting disaster prevention, solar energy forecasting and transportation. However, cloud mask nowcasting remains challenging because meteorological data exhibit irregular temporal and spatial variations, including fine-scale structures, and often suffer from highly skewed precipitation intensity distributions. Existing methods struggle to capture complex spatiotemporal dynamics and preserve fine-scale structures due to limitations in handling sparse data from numerical weather prediction (NWP) model. To address these issues, we propose an asymmetric dual-branch non-causal mamba U-Net (ADNM-UNet) featuring three key components: (1) The Asymmetric Dual-branch Non-causal Mamba (ADNM) implements a novel asymmetric bidirectional modeling framework that resolves directional bias in conventional Mamba architectures. This design preserves precise cloud boundary delineation while capturing long-range spatiotemporal dependencies in sparse data from NWP. (2) The Multi-scale Attention Enhancement Module (MAEM) enhances discriminative feature representation and suppresses spectral redundancy through anisotropic convolution kernels and hybrid pooling. This mechanism significantly improves edge retention in precipitation systems while attenuating atmospheric noise interference. (3) Complementing these advancements, the Wavelet Decomposition and Fusion Module (WDFM) maintains cloud contour integrity across scales through multiresolution decomposition. Extensive experiments demonstrate that ADNM-UNet outperforms existing methods across all metrics, achieving 27.96% improvement in CSI and 22.89% in HSS for metrics over the best performing baseline models at high intensity scenarios. Our project is open source and available on GitHub at: https://github.com/kanyu369/ADNM-UNet.

Precipitation nowcasting is a challenging task in the context of global climate variability. However, existing radar echo or numerical weather prediction data methods lack deep modeling between echograms at different time points and have difficulty in accurately capturing irregular variations and small-scale features of precipitable clouds. To address these challenges, we propose for the first time a U-Net short-term precipitation prediction network based on vision Mamba technology for the precipitation nowcasting mission, named Mamba-UNet. Specifically, Mamba-UNet includes two core modules: the dual-branch Mamba fusion module and the multiscale spatiotemporal attention module. Finally, we propose a loss function namely dynamic quantile weighted loss to address the problem of imbalanced precipitation intensity distribution. To validate the capacity of the proposed method, the experiments were conducted on an analysis dataset of the local analysis and prediction system model in a specific region of East China. The experimental results show that our proposed Mamba-UNet has the best overall performance.

Abstract Accurate prediction of traffic flow plays an important role in maintaining traffic order and traffic safety, which is a key task in the application of intelligent transportation systems (ITS). However, the urban road network has complex dynamic spatial correlation and nonlinear temporal correlation, and achieving accurate traffic flow prediction is a highly challenging task. Traditional methods use sensors deployed on roads to construct the spatial structure of the road network and capture spatial information by graph convolution. However, they ignore that the spatial correlation between nodes is dynamically changing, and using a fixed adjacency matrix cannot reflect the real road spatial structure. To overcome these limitations, this paper proposes a new spatial-temporal deep learning model: gated fusion adaptive graph neural network (GFAGNN). GFAGNN first extracts long-term dependencies on raw data through stacking expansion causal convolution, Then the spatial features of the dynamics are learned by adaptive graph attention network and adaptive graph convolutional network respectively, Finally the fused information is passed through a lightweight channel attention to extract temporal features. The experimental results on two public data sets show that our model can effectively capture the spatiotemporal correlation in traffic flow prediction. Compared with GWNET-conv model on METR-LA dataset, the three indexes in the 60-minute task prediction improved by 2.27%,2.06% and 2.13%, respectively.

Hyperspectral image (HSI) classification is an essential task in remote sensing with substantial practical significance. However, most existing convolutional neural network (CNN)-based classification methods focus only on local spatial features while neglecting global spectral dependencies. Meanwhile, Transformer-based methods exhibit robust capabilities for global spectral feature modeling but struggle to extract local spatial features effectively. To fully exploit the local spatial feature extraction capabilities of CNN-based networks and the global spectral feature extraction capabilities of Transformer-based networks, this paper proposes a dual-branch convolutional Transformer method with efficient interactive self-attention for hyperspectral image classification, namely the dual-branch convolutional Transformer network (DCTN), which can aggregate local and global spatial-spectral features fully. Specifically, DCTN includes two core modules: the spatial-spectral fusion projection module and the efficient interactive self-attention module. The former utilizes 3D convolution with adaptive pooling and 2D group convolution with residual connection to parallel extract fused and grouped spatial-spectral features, respectively. The latter performs efficient interactive self-attention across height, width and spectral dimensions, enabling deep fusion of spatial-spectral features. Extensive experiments on three real HSI datasets demonstrate that the proposed DCTN method outperforms existing classification methods, yielding state-of-the-art classification performance. The code is available at https://github.com/AllFever/DeepHyperX-DCTN for reproducibility.

Deep learning plays a crucial role in hyperspectral image (HSI) classification, with the Transformer being highly favored by researchers due to its exceptional ability to model long-range dependencies. However, the Transformer necessitates a substantial amount of labeled training samples to train its numerous parameters, exacerbating the challenge of training an effective HSI classification Transformer model, particularly given the inherent scarcity of HSI data. Therefore, we propose a novel method for HSI classification, termed multiscale spatial-spectral masked Transformer with local contrastive learning (MSMT-LCL). This method consists of two stages: self-supervised pretraining and supervised fine-tuning. Initially, we utilize the multiscale augmented feature mapping module (MAFM) to project original HSI data into two mixed-scale feature maps, which are then separately fed into two masked Transformer branches for reconstruction. To facilitate the model in learning the dependency relationships between central pixel land-cover information and neighboring land cover, we introduce a novel mask strategy based on center-patch. Furthermore, in the pretraining stage, we integrate local contrastive learning (LCL) to enable the model to focus on local center information at varying scales. Upon completion of pretraining, the network undergoes fine-tuning to obtain feature maps at two different scales. Subsequently, we devise a novel adaptive multiscale feature fusion module (AMFM) to adaptively aggregate these two features and produce the final classification results. Extensive experiments on three real datasets demonstrate the superiority of our proposed MSMT-LCL method over several state-of-the-art HSI classification methods.

In recent years, ride-sharing has gained popularity as a daily means of transportation. The primary challenge for large-scale online ride-sharing platforms is to design an efficient fleet management policy that reallocates vehicles to appropriate regions to receive orders, thereby improving the platform’s cumulative revenue and order response rate. Combinatorial optimization algorithms and reinforcement learning methods are commonly employed for this task, but they typically learn a unified repositioning policy for all regions. However, different regions, such as hot and cold zones, may require different repositioning policies due to varying travel patterns. In this paper, we propose a multi-agent mixed hierarchical reinforcement learning approach, called MIX-H, for efficient large-scale fleet management by formulating it as a Markov decision process. MIX-H adopts multi-level controllers, including a leader controller and follower controller, for multi-level action learning. The leader controller plans the goal to be executed by the follower controller. Additionally, to improve the algorithm’s stability, we introduce a MIX module to compute the total value of joint action. Finally, experiments on real-world datasets demonstrate that the proposed method outperforms the state-of-the-art methods.

Although deep learning models have been successfully adopted in many applications, they are facing challenges to be deployed on energy-limited devices (e.g., some mobile devices, etc.) due to their high computation complexity. In this paper, we focus on reducing the costs of Gated Recurrent Units (GRUs) for time-series prediction tasks and we propose a new pruning method that can recognize and remove the neural connections that have little influence on the network loss, using a controllable threshold on the absolute value of the pre-trained GRU weights. This is different from existing approaches which usually try to find and preserve the connections with large weight values. We further propose a sparse-connection GRU model (SCGRU) that only needs a one-time pruning (with fine-tuning), rather than using multiple prune-retrain cycles. A large number of experimental results demonstrate that the proposed method is able to largely reduce the storage and computation costs while achieving the state-of-arts performance in two datasets. Code is available ( https://github.com/imLingo/SCGRU).

Road extraction from aerial images is a hot research topic. With the advancement of convolutional neural network (CNN), several CNN-based road detection methods have been developed. However, most of them do not make full use of the hierarchical features from the original aerial images. In this paper, we propose a novel residual dense U-Net (RDUN), a semantic segmentation network which combines the strengths of residual learning, DenseNet, and U-Net, to overcome the drawback. Our proposed RDUN can fully exploit the hierarchical features from all the convolutional layers, which utilizes the residual dense blocks (RDB) to build up a U-Net architecture. The benefits of our model are two-fold. First, by using the RDB abundant local features can be extracted and fused effectively. Second, based the local features, hierarchical features are constructed by shortcut connections between layers in RDB. Extensive experiments are carried out on a real-world road detection dataset and the results demonstrate the proposed RDUN outperforms state-of-the-art competitors.

Road information extraction based on aerial images is a critical task for many applications, and it has attracted considerable attention from researchers in the field of remote sensing. The problem is mainly composed of two subtasks, namely, road detection and centerline extraction. Most of the previous studies rely on multistage-based learning methods to solve the problem. However, these approaches may suffer from the well-known problem of propagation errors. In this paper, we propose a novel deep learning model, recurrent convolution neural network U-Net (RCNN-UNet), to tackle the aforementioned problem. Our proposed RCNN-UNet has three distinct advantages. First, the end-to-end deep learning scheme eliminates the propagation errors. Second, a carefully designed RCNN unit is leveraged to build our deep learning architecture, which can better exploit the spatial context and the rich low-level visual features. Thereby, it alleviates the detection problems caused by noises, occlusions, and complex backgrounds of roads. Third, as the tasks of road detection and centerline extraction are strongly correlated, a multitask learning scheme is designed so that two predictors can be simultaneously trained to improve both effectiveness and efficiency. Extensive experiments were carried out based on two publicly available benchmark data sets, and nine state-of-the-art baselines were used in a comparative evaluation. Our experimental results demonstrate the superiority of the proposed RCNN-UNet model for both the road detection and the centerline extraction tasks.

Deep learning has achieved great successes in conventional computer vision tasks. In this paper, we exploit deep learning techniques to address the hyperspectral image classification problem. In contrast to conventional computer vision tasks that only examine the spatial context, our proposed method can exploit both spatial context and spectral correlation to enhance hyperspectral image classification. In particular, we advocate four new deep learning models, namely, 2-D convolutional neural network (2-D-CNN), 3-D-CNN, recurrent 2-D CNN (R-2-D-CNN), and recurrent 3-D-CNN (R-3-D-CNN) for hyperspectral image classification. We conducted rigorous experiments based on six publicly available data sets. Through a comparative evaluation with other state-of-the-art methods, our experimental results confirm the superiority of the proposed deep learning models, especially the R-3-D-CNN and the R-2-D-CNN deep learning models.

Heterogeneous data sources and multi-label are two important characteristics of protein function prediction. They describe protein data from two different aspects. However, it is of considerable challenge to integrate multiple data sources and multi-label simultaneously for predicting protein functions, especially when there are only a limited number of labeled proteins. In this paper, we propose a generative model with hypergraph regularizers algorithm, called GMHR, for predicting proteins with multiple functions. The GMHR algorithm integrates all data sources that are available, including protein attribute features, interaction networks, label correlations, and unlabeled data. Experimental results on the real-world datasets predicting the functions of proteins demonstrate the superiority of our proposed method compared with the state-of-the-art baselines.

Kmeans-type clustering aims at partitioning a data set into clusters such that the objects in a cluster are compact and the objects in different clusters are well separated. However, most kmeans-type clustering algorithms rely on only intracluster compactness while overlooking intercluster separation. In this paper, a series of new clustering algorithms by extending the existing kmeans-type algorithms is proposed by integrating both intracluster compactness and intercluster separation. First, a set of new objective functions for clustering is developed. Based on these objective functions, the corresponding updating rules for the algorithms are then derived analytically. The properties and performances of these algorithms are investigated on several synthetic and real-life data sets. Experimental studies demonstrate that our proposed algorithms outperform the state-of-the-art kmeans-type clustering algorithms with respect to four metrics: accuracy, RandIndex, Fscore, and normal mutual information.