Semantic Neural Radiance Fields for Multi-Date Satellite Data
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866909508067917824 |
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| author | Wagner, Valentin Bullinger, Sebastian Bodensteiner, Christoph Arens, Michael |
| author_facet | Wagner, Valentin Bullinger, Sebastian Bodensteiner, Christoph Arens, Michael |
| contents | In this work we propose a satellite specific Neural Radiance Fields (NeRF) model capable to obtain a three-dimensional semantic representation (neural semantic field) of the scene. The model derives the output from a set of multi-date satellite images with corresponding pixel-wise semantic labels. We demonstrate the robustness of our approach and its capability to improve noisy input labels. We enhance the color prediction by utilizing the semantic information to address temporal image inconsistencies caused by non-stationary categories such as vehicles. To facilitate further research in this domain, we present a dataset comprising manually generated labels for popular multi-view satellite images. Our code and dataset are available at https://github.com/wagnva/semantic-nerf-for-satellite-data. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2502_16992 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Semantic Neural Radiance Fields for Multi-Date Satellite Data Wagner, Valentin Bullinger, Sebastian Bodensteiner, Christoph Arens, Michael Computer Vision and Pattern Recognition In this work we propose a satellite specific Neural Radiance Fields (NeRF) model capable to obtain a three-dimensional semantic representation (neural semantic field) of the scene. The model derives the output from a set of multi-date satellite images with corresponding pixel-wise semantic labels. We demonstrate the robustness of our approach and its capability to improve noisy input labels. We enhance the color prediction by utilizing the semantic information to address temporal image inconsistencies caused by non-stationary categories such as vehicles. To facilitate further research in this domain, we present a dataset comprising manually generated labels for popular multi-view satellite images. Our code and dataset are available at https://github.com/wagnva/semantic-nerf-for-satellite-data. |
| title | Semantic Neural Radiance Fields for Multi-Date Satellite Data |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2502.16992 |