D2-CDIG conditions diffusion models on DEM and cloud-fog priors to generate controlled remote sensing images with decoupled terrain and atmospheric control.
Advantages of the mean absolute error (mae) over the root mean square error (rmse) in assessing average model performance
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 2verdicts
UNVERDICTED 2representative citing papers
Synthetic flight data generated by TVAE and Gaussian Copula models supports flight delay prediction models with accuracy comparable to real data.
citing papers explorer
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D2-CDIG: Controlled Diffusion Remote Sensing Image Generation with Dual Priors of DEM and Cloud-Fog
D2-CDIG conditions diffusion models on DEM and cloud-fog priors to generate controlled remote sensing images with decoupled terrain and atmospheric control.
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Synthetic Flight Data Generation Using Generative Models
Synthetic flight data generated by TVAE and Gaussian Copula models supports flight delay prediction models with accuracy comparable to real data.