GeoDiT: Point-Conditioned Diffusion Transformer for Satellite Image Synthesis
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arXiv
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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866915829038186496 |
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| author | Sastry, Srikumar Cher, Dan Wei, Brian Dhakal, Aayush Khanal, Subash Gupta, Dev Jacobs, Nathan |
| author_facet | Sastry, Srikumar Cher, Dan Wei, Brian Dhakal, Aayush Khanal, Subash Gupta, Dev Jacobs, Nathan |
| contents | We introduce GeoDiT, a diffusion transformer designed for text-to-satellite image generation with point-based control. Existing controlled satellite image generative models often require pixel-level maps that are time-consuming to acquire, yet semantically limited. To address this limitation, we introduce a novel point-based conditioning framework that controls the generation process through the spatial location of the points and the textual description associated with each point, providing semantically rich control signals. This approach enables flexible, annotation-friendly, and computationally simple inference for satellite image generation. To this end, we introduce an adaptive local attention mechanism that effectively regularizes the attention scores based on the input point queries. We systematically evaluate various domain-specific design choices for training GeoDiT, including the selection of satellite image representation for alignment and geolocation representation for conditioning. Our experiments demonstrate that GeoDiT achieves impressive generation performance, surpassing the state-of-the-art remote sensing generative models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_02172 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | GeoDiT: Point-Conditioned Diffusion Transformer for Satellite Image Synthesis Sastry, Srikumar Cher, Dan Wei, Brian Dhakal, Aayush Khanal, Subash Gupta, Dev Jacobs, Nathan Computer Vision and Pattern Recognition We introduce GeoDiT, a diffusion transformer designed for text-to-satellite image generation with point-based control. Existing controlled satellite image generative models often require pixel-level maps that are time-consuming to acquire, yet semantically limited. To address this limitation, we introduce a novel point-based conditioning framework that controls the generation process through the spatial location of the points and the textual description associated with each point, providing semantically rich control signals. This approach enables flexible, annotation-friendly, and computationally simple inference for satellite image generation. To this end, we introduce an adaptive local attention mechanism that effectively regularizes the attention scores based on the input point queries. We systematically evaluate various domain-specific design choices for training GeoDiT, including the selection of satellite image representation for alignment and geolocation representation for conditioning. Our experiments demonstrate that GeoDiT achieves impressive generation performance, surpassing the state-of-the-art remote sensing generative models. |
| title | GeoDiT: Point-Conditioned Diffusion Transformer for Satellite Image Synthesis |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2603.02172 |