In this work, the seasonal predictive capabilities of Neural Radiance Fields (NeRF) applied to satellite images are investigated. Focusing on the utilization of satellite data, the study explores how Sat-NeRF, a novel approach in computer vision, performs in predicting seasonal variations across different months. Through comprehensive analysis and visualization, the study examines the model's ability to capture and predict seasonal changes, highlighting specific challenges and strengths. Results showcase the impact of the sun direction on predictions, revealing nuanced details in seasonal transitions, such as snow cover, color accuracy, and texture representation in different landscapes. Given these results, we propose Planet-NeRF, an extension to Sat-NeRF capable of incorporating seasonal variability through a set of month embedding vectors. Comparative evaluations reveal that Planet-NeRF outperforms prior models in the case where seasonal changes are present. The extensive evaluation combined with the proposed method offers promising avenues for future research in this domain.

翻译：本研究探讨了应用于卫星图像的神经辐射场（NeRF）的季节性预测能力。聚焦于卫星数据的利用，该研究探索了计算机视觉领域的新方法Sat-NeRF在预测不同月份季节性变化方面的表现。通过全面的分析和可视化，研究检验了模型捕捉和预测季节变化的能力，并突出了其特定的挑战和优势。结果展示了太阳方向对预测的影响，揭示了季节转换中的细微细节，如不同景观中的积雪覆盖、色彩准确性和纹理表征。基于这些结果，我们提出了Planet-NeRF，这是Sat-NeRF的一个扩展，能够通过一组月份嵌入向量来纳入季节性变化。比较评估表明，在存在季节性变化的情况下，Planet-NeRF的表现优于先前的模型。广泛的评估结合所提出的方法，为该领域的未来研究提供了有前景的途径。