yjwong1999/RSGuidedDiffusion
[IEEE BigData 2024] Cross-City Building Instance Segmentation: From More Data to Diffusion-Augmentation
This project helps remote sensing analysts or urban planners generate realistic satellite-like images from basic land cover segmentation masks. You input existing segmentation maps (like outlines of buildings or land types), and it outputs new, synthetic satellite images that look real. This allows you to create more training data to improve the accuracy of models that automatically identify and segment buildings from satellite imagery, especially when real image data is scarce.
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Use this if you need to create more diverse training data for remote sensing building segmentation tasks, especially when limited by the availability of actual satellite imagery, but you have segmentation masks.
Not ideal if you're looking to directly segment buildings from satellite images without using synthetic data, or if you don't have existing segmentation masks to guide the image generation process.
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Language
Python
License
MIT
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Last pushed
Jun 12, 2025
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