USF-Net: A Unified Spatiotemporal Fusion Network for Ground-Based Remote Sensing Cloud Image Sequence Extrapolation

Ground-based remote sensing cloud image sequence extrapolation is a key research area in the development of photovoltaic power systems. However, existing approaches exhibit several limitations:(1)they primarily rely on static kernels to augment feature information, lacking adaptive mechanisms to extract features at varying resolutions dynamically;(2)temporal guidance is insufficient, leading to suboptimal modeling of long-range spatiotemporal dependencies; and(3)the quadratic computational cost of attention mechanisms is often overlooked, limiting efficiency in practical deployment. To address these challenges, we propose USF-Net, a Unified Spatiotemporal Fusion Network that integrates adaptive large-kernel convolutions and a low-complexity attention mechanism, combining temporal flow information within an encoder-decoder framework. Specifically, the encoder employs three basic layers to extract features. Followed by the USTM, which comprises:(1)a SiB equipped with a SSM that dynamically captures multi-scale contextual information, and(2)a TiB featuring a TAM that effectively models long-range temporal dependencies while maintaining computational efficiency. In addition, a DSM with a TGM is introduced to enable unified modeling of temporally guided spatiotemporal dependencies. On the decoder side, a DUM is employed to address the common "ghosting effect." It utilizes the initial temporal state as an attention operator to preserve critical motion signatures. As a key contribution, we also introduce and release the ASI-CIS dataset. Extensive experiments on ASI-CIS demonstrate that USF-Net significantly outperforms state-of-the-art methods, establishing a superior balance between prediction accuracy and computational efficiency for ground-based cloud extrapolation. The dataset and source code will be available at https://github.com/she1110/ASI-CIS.

MPCM-Net: Multi-scale network integrates partial attention convolution with Mamba for ground-based cloud image segmentation

Ground-based cloud image segmentation is a critical research domain for photovoltaic power forecasting. Current deep learning approaches primarily focus on encoder-decoder architectural refinements. However, existing methodologies exhibit several limitations:(1)they rely on dilated convolutions for multi-scale context extraction, lacking the partial feature effectiveness and interoperability of inter-channel;(2)attention-based feature enhancement implementations neglect accuracy-throughput balance; and (3)the decoder modifications fail to establish global interdependencies among hierarchical local features, limiting inference efficiency. To address these challenges, we propose MPCM-Net, a Multi-scale network that integrates Partial attention Convolutions with Mamba architectures to enhance segmentation accuracy and computational efficiency. Specifically, the encoder incorporates MPAC, which comprises:(1)a MPC block with ParCM and ParSM that enables global spatial interaction across multi-scale cloud formations, and (2)a MPA block combining ParAM and ParSM to extract discriminative features with reduced computational complexity. On the decoder side, a M2B is employed to mitigate contextual loss through a SSHD that maintains linear complexity while enabling deep feature aggregation across spatial and scale dimensions. As a key contribution to the community, we also introduce and release a dataset CSRC, which is a clear-label, fine-grained segmentation benchmark designed to overcome the critical limitations of existing public datasets. Extensive experiments on CSRC demonstrate the superior performance of MPCM-Net over state-of-the-art methods, achieving an optimal balance between segmentation accuracy and inference speed. The dataset and source code will be available at https://github.com/she1110/CSRC.