  As a result of Shadow NeRF and Sat-NeRF, it is possible to take the solar
angle into account in a NeRF-based framework for rendering a scene from a novel
viewpoint using satellite images for training. Our work extends those
contributions and shows how one can make the renderings season-specific. Our
main challenge was creating a Neural Radiance Field (NeRF) that could render
seasonal features independently of viewing angle and solar angle while still
being able to render shadows. We teach our network to render seasonal features
by introducing one more input variable -- time of the year. However, the small
training datasets typical of satellite imagery can introduce ambiguities in
cases where shadows are present in the same location for every image of a
particular season. We add additional terms to the loss function to discourage
the network from using seasonal features for accounting for shadows. We show
the performance of our network on eight Areas of Interest containing images
captured by the Maxar WorldView-3 satellite. This evaluation includes tests
measuring the ability of our framework to accurately render novel views,
generate height maps, predict shadows, and specify seasonal features
independently from shadows. Our ablation studies justify the choices made for
network design parameters.
