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Recent advances in remote sensing technologies have highlighted their capability for accurate classification of local climate zones (LCZs). However, traditional methods using convolutional neural networks (CNNs) often fall short of effectively incorporating prior knowledge of ground objects. In addition, data sources such as Sentinel-2 struggle with capturing detailed information on ground objects. To address these issues, we introduce a novel data fusion approach that combines high-resolution Google imagery, which provides ground object priors, with Sentinel-2 multispectral imagery. Our method, the Dual-stream Fusion framework for LCZ classification (DF4LCZ), merges instance-based location features from Google imagery and spatial-spectral features from Sentinel-2. This framework is enhanced by a graph convolutional network (GCN) module, powered by the segment anything model (SAM), to improve feature extraction from Google imagery. Concurrently, a 3D-CNN architecture is utilized to process the spectral-spatial features of Sentinel-2 imagery. The effectiveness of DF4LCZ is demonstrated through experiments conducted on a specialized multisource remote sensing image dataset for LCZ classification. The related code and dataset are available at https://github.com/ctrlovefly/DF4LCZ.

The presence of cloud layers severely compromises the quality and effectiveness of optical remote sensing (RS) images. However, existing deep-learning (DL)-based cloud removal (CR) techniques, which usually take the fidelity-driven losses as constraints, e.g., $L_{1}$ or $L_{2}$ losses, tend to generate smooth results, often failing to reconstruct visually pleasing results and cause semantic loss. To tackle this challenge, this work proposes to encompass enhancements at the data and methodology fronts. On the data side, an ultra-resolution benchmark named CUHK cloud removal (CUHK-CR) of 0.5 m spatial resolution is established. This benchmark incorporates rich detailed textures and diverse cloud coverage, serving as a robust foundation for designing and assessing CR models. From the methodology perspective, a novel diffusion-based framework for CR named diffusion enhancement (DE) is introduced. This framework aims to gradually recover texture details, leveraging a reference visual prior providing foundational structure of the images to enhance inference accuracy. Additionally, a weight allocation (WA) network is developed to dynamically adjust the weights for feature fusion, thereby further improving performance, particularly in the context of ultra-resolution image generation. Furthermore, a coarse-to-fine training strategy is applied to effectively expedite training convergence while reducing the computational complexity required to handle ultra-resolution images. Extensive experiments on the newly established CUHK-CR and existing datasets such as RICE confirm that the proposed DE framework outperforms existing DL-based methods in terms of both perceptual quality and signal fidelity.