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| language: |
| - en |
| size_categories: |
| - 100K<n<1M |
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| Dataset Summary |
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| DeepShade is a multimodal dataset designed for shade simulation via text-conditioned image generation. It captures realistic outdoor scenes and their corresponding shade conditions over time, enabling supervised training of diffusion-based models that can simulate sun-shade transitions based on spatial layout and temporal context. |
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| The dataset was introduced in the DeepShade project (IJCAI 2025 submission), and it supports research in text-to-image generation. |
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| Dataset Files Structure: |
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| Each city has a dedicated zip folder which contains source, target and the test and train json files |
| There is one common zip file for all the satellite images of all the cities |
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| Dataset Structure |
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| Data Modality: Multimodal (Image, Text, Time) |
| Number of samples: ~100,000 |
| Resolution: 1024×1024 images |
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| Formats: |
| .png images (rendered) |
| .json metadata files with the following fields: |
| 1. Source image file path |
| 2. Target image file path |
| 3. Prompt |
