| | --- |
| | license: apache-2.0 |
| | configs: |
| | - config_name: default |
| | data_files: |
| | - split: train |
| | path: data/train-* |
| | dataset_info: |
| | features: |
| | - name: condition |
| | dtype: image |
| | - name: lat |
| | dtype: float64 |
| | - name: lon |
| | dtype: float64 |
| | - name: heading |
| | dtype: float64 |
| | - name: elevation |
| | dtype: float64 |
| | - name: panoid |
| | dtype: string |
| | splits: |
| | - name: train |
| | num_bytes: 12545139637.182 |
| | num_examples: 25503 |
| | download_size: 12541077692 |
| | dataset_size: 12545139637.182 |
| | --- |
| | |
| | # Satellite to GroundScape - Large-scale Consistent Ground View Generation from Satellite Views |
| |
|
| | [**🌐 Homepage**](https://gdaosu.github.io/sat2groundscape/) | [**📖 arXiv**](https://arxiv.org/abs/2504.15786) |
| |
|
| |
|
| | ## Introduction |
| | Generating consistent ground-view images from satellite imagery is challenging, primarily due to the large discrepancies in viewing angles and resolution between satellite and ground-level domains. Previous efforts mainly concentrated on single-view generation, often resulting in inconsistencies across neighboring ground views. In this work, we propose a novel cross-view synthesis approach designed to overcome these challenges by ensuring consistency across ground-view images generated from satellite views. Our method, based on a fixed latent diffusion model, introduces two conditioning modules: satellite-guided denoising, which extracts high-level scene layout to guide the denoising process, and satellite-temporal denoising, which captures camera motion to maintain consistency across multiple generated views. We further contribute a large-scale satellite-ground dataset containing over 100,000 perspective pairs to facilitate extensive ground scene or video generation. Experimental results demonstrate that our approach outperforms existing methods on perceptual and temporal metrics, achieving high photorealism and consistency in multi-view outputs. |
| |
|
| | ## Description |
| | The Sat2GroundScape contains 25,503 pairs of satellite-ground data in panaroma format. Including: |
| | * <strong>condition</strong>: [512x1024x3] satellite rgb texture, rendered from ground-level camera. |
| | * <strong>lat, lon</strong>: latitude, longtitude of the ground image. |
| | * <strong>elevation</strong>: elevation (meters) of the ground image |
| | * <strong>heading</strong>: the heading (degrees) of the ground image in panaroma format. |
| | * <strong>pano_id</strong>: used for downloading corresponding GT ground-view image. |
| | |
| | ## Downloading Ground-view image |
| | Each GT ground-view image is associated with a unique ID, pano_id. Please refer to https://github.com/robolyst/streetview for downloading the original ground-view image (512x1024x3). |
| |
|
| | ``` |
| | from streetview import get_streetview |
| | |
| | image = get_streetview( |
| | pano_id="z80QZ1_QgCbYwj7RrmlS0Q", |
| | api_key=GOOGLE_MAPS_API_KEY, |
| | ) |
| | |
| | image.save("image.jpg", "jpeg") |
| | ``` |
| |
|
| | ## Citation |
| | **BibTex:** |
| | ```bibtex |
| | @article{xu2025satellite, |
| | title={Satellite to GroundScape--Large-scale Consistent Ground View Generation from Satellite Views}, |
| | author={Xu, Ningli and Qin, Rongjun}, |
| | journal={arXiv preprint arXiv:2504.15786}, |
| | year={2025} |
| | } |
| | ``` |
| |
|
