Addressing the deficiencies of existing deep learning models in local spatio-temporal dependency modeling and local-global feature fusion, a new prediction model LFAGCSA (Local Fusion Adaptive Graph Convolution And Self-Attention) is proposed. The model achieves refined modeling through a multilevel architecture: (1) temporal, feature, and spatial embedding techniques are used in the data processing stage to map the original data into a high-dimensional representation space to enhance feature separability; (2) dynamic adaptive graph convolution and gated deep convolutional feedforward network (GDFN) are combined in the local feature extraction module to achieve adaptive modeling of the local road network structure; (3) temporal and spatial self-attention mechanisms are integrated in the global feature capture module to model the global dependencies across time and regions, respectively; (4) the local fine-grained features are organically combined with the global spatial-temporal features in the fusion output layer, and ultimately outputting the multistep prediction results through the fully connected layer. The experimental results on four real datasets demonstrate that LFAGCSA outperforms the benchmark model in all evaluation criteria, achieving accurate prediction of future traffic flow.

Traffic flow prediction is an important technology in intelligent transportation systems (ITS) . Accurate traffic prediction can reduce congestion and improve traffic efficiency. However, traffic flow data contains complex temporal relationships, and capturing dynamic traffic spatial relationships is a challenge. In order to improve the prediction accuracy, a dynamic graph multi temporal perspectives attention network (DGMAN) is proposed,based on the spatiotemporal data of traffic flow. The model uses a dynamic graph learning module (DGLM) to extract the dynamic relationship information between traffic nodes in traffic data by establishing a dynamic graph. In complex temporal data, the multi temporal perspectives attention mechanism (MtpA) captures the temporal dependence of traffic flow and mines potential temporal relationships. Finally, the proposed model is tested on 4 real-world datasets. Compared with the baseline models, DGMAN achieves the best performance in the mean absolute error (MAE) , root mean square error (RMSE) and mean absolute percentage error (MAPE) evaluation metrics.

Change detection in buildings holds significant importance in the fields of remote sensing image processing and pattern recognition. However, data annotation has always been a prominent challenge in the application of deep learning algorithms, especially in change detection scenarios. To address the data annotation challenges in deep learning-based change detection algorithms, this paper proposes an innovative semi-supervised learning method. This method employs a Siamese network that fuses bi-temporal features for feature extraction and constructs a teacher-student network framework for semi-supervised model training. To further enhance the accuracy of semi-supervised change detection, this paper introduces random perturbations in deep features to achieve consistency regularization. Additionally, on the level of image deep features, the paper proposes a method for forming decision boundaries by capturing differences in bi-temporal image features to distinguish changes in bi-temporal images. This method achieved Intersection over Union (IoU) scores of 83.04% and 85.57% on the Levir-CD and WHU Building datasets, respectively. Experimental results show that the proposed method can achieve performance levels close to fully supervised training with a limited amount of labeled data.