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Found 6 papers, 3 papers with code
DaYu: Data-Driven Model for Geostationary Satellite Observed Cloud Images Forecasting
Unlike existing data-driven weather forecasting models, DaYu is specifically designed for geostationary satellite observations, with a temporal resolution of 0. 5 hours and a spatial resolution of ${0. 05}^\circ$ $\times$ ${0. 05}^\circ$.
Generative Diffusion Model-based Downscaling of Observed Sea Surface Height over Kuroshio Extension since 2000
Satellite altimetry has been widely utilized to monitor global sea surface dynamics, enabling investigation of upper ocean variability from basin-scale to localized eddy ranges.
Quantitative causality, causality-guided scientific discovery, and causal machine learning
It has been said, arguably, that causality analysis should pave a promising way to interpretable deep learning and generalization.
LSRFormer: Efficient Transformer Supply Convolutional Neural Networks with Global Information for Aerial Image Segmentation
Convolutional Neural Networks (CNNs) can capture local context information well but cannot model the global dependencies.
DSAT-Net: Dual Spatial Attention Transformer for Building Extraction from Aerial Images
The local attention path (LAP) uses efficient stripe convolution to generate local attention, which can alleviate the loss of information caused by down-sampling operation in the GAP and supplement the spatial details.
Extracting Buildings In Remote Sensing Images
Semantic Segmentation
SDSC-UNet: Dual Skip Connection ViT-based U-shaped Model for Building Extraction
Furthermore, unlike the previous single-skip-connection structure of U-shaped methods, we build a novel dual skip connection structure inside the model.
