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.

翻译：本研究提出了一种专用于卫星数据的神经辐射场（NeRF）模型，该模型能够从场景中获取三维语义表示（神经语义场）。该模型通过一组具有对应像素级语义标签的多时相卫星图像推导出输出结果。我们验证了所提方法的鲁棒性及其改进带噪声输入标签的能力。通过利用语义信息处理由非静态类别（如车辆）引起的时相图像不一致问题，我们增强了颜色预测的准确性。为了推动该领域的进一步研究，我们发布了一个数据集，包含针对流行多视角卫星图像的人工标注标签。我们的代码与数据集可在 https://github.com/wagnva/semantic-nerf-for-satellite-data 获取。