Precipitation nowcasting is pivotal in monitoring extreme weather events and issuing early warnings for meteorological disasters. However, the inherent complexity of precipitation systems, coupled with their nonlinear spatiotemporal evolution, poses significant challenges for traditional numerical weather prediction methods in capturing multi-scale details effectively. Existing deep learning models similarly struggle to simultaneously capture local multi-scale features and global long-term spatiotemporal dependencies. To tackle this challenge, we propose SwinNowcast, a deep learning model based on the Swin Transformer architecture. Through the novel design of a multi-scale feature balancing module (M-FBM), the model dynamically integrates local-scale features with global spatiotemporal dependencies. Specifically, the multi-scale convolutional block attention module (MSCBAM) captures local multi-scale features, while the gated attention feature fusion unit (GAFFU) adaptively regulates the fusion intensity, thereby enhancing spatial structure and temporal continuity in a synergistic manner. Experiments were performed on the precipitation dataset from the Royal Netherlands Meteorological Institute (KNMI) under thresholds of 0.5 mm, 5 mm, and 10 mm. The results indicate that SwinNowcast surpasses six state-of-the-art approaches regarding the critical success index (CSI) and the Heidke skill score (HSS), while markedly reducing the false alarm rate (FAR). The proposed model holds substantial practical value in applications such as short-term heavy rainfall monitoring and urban flood early warning, offering effective technological support for meteorological disaster mitigation.

Satellite cloud images exhibit complex multidimensional characteristics, including spectral, textural, and spatiotemporal dynamics. The temporal evolution of cloud systems plays a crucial role in accurate classification, particularly under the coexistence of multiple weather systems. However, most existing models—such as those based on convolutional neural networks (CNNs), Transformer architectures, and their variants like Swin Transformer—primarily focus on spatial modeling of static images and do not explicitly incorporate temporal information, thereby limiting their ability to effectively integrate spatiotemporal features. To address this limitation, we propose SIG-ShapeFormer, a novel classification model specifically designed for satellite cloud images with temporal continuity. To the best of our knowledge, this work is the first to transform satellite cloud data into multivariate time series and introduce a unified framework for multi-scale and multimodal feature fusion. SIG-Shapeformer consists of three core components: (1) a Shapelet-based module that captures discriminative and interpretable local temporal patterns; (2) a multi-scale Inception module combining 1D convolutions and Transformer encoders to extract temporal features across different scales; and (3) a differentially enhanced Gramian Angular Summation Field (GASF) module that converts time series into 2D texture representations, significantly improving the recognition of cloud internal structures. Experimental results demonstrate that SIG-ShapeFormer achieves a classification accuracy of 99.36% on the LSCIDMR-S dataset, outperforming the original ShapeFormer by 2.2% and outperforming other CNN- or Transformer-based models. Moreover, the model exhibits strong generalization performance on the UCM remote sensing dataset and several benchmark tasks from the UEA time-series archive. SIG-Shapeformer is particularly suitable for remote sensing applications involving continuous temporal sequences, such as extreme weather warnings and dynamic cloud system monitoring. However, it relies on temporally coherent input data and may perform suboptimally when applied to datasets with limited or irregular temporal resolution.

Semantic segmentation of remote sensing imagery is crucial for applications such as land resource management and urban planning, yet it remains challenging due to low intra-class variation, ambiguous boundaries, and the coexistence of multi-scale geospatial features. To tackle these issues, we propose GS-USTNet, a novel architecture that enhances both feature representation and boundary recovery. First, we introduce a Global–Local Adaptive Convolution (GLAConv) module that dynamically fuses global contextual cues with local responses to generate content-aware convolutional weights, thereby improving feature discriminability. Second, we design a Skip-Guided Attention (SGA) mechanism that leverages spatial–channel joint attention to guide the decoder, effectively mitigating attention dispersion in complex scenes or under class imbalance and significantly sharpening object boundaries. Built upon the efficient USTNet framework, our model achieves substantial performance gains without compromising computational efficiency. Extensive experiments on benchmark datasets demonstrate that GS-USTNet achieves consistent improvements over the original USTNet, with gains of approximately 3.5% in overall accuracy and 6.0% in F1-score across datasets. Ablation studies further confirm the effectiveness of the proposed GLAConv and SGA modules. This work provides an efficient and robust approach for fine-grained semantic segmentation of high-resolution remote sensing imagery.