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As a result of Shadow NeRF and Sat-NeRF, it is possible to take the solarangle into account in a NeRF-based framework for rendering a scene from a novelviewpoint using satellite images for training. Our work extends thosecontributions and shows how one can make the renderings season-specific. Ourmain challenge was creating a Neural Radiance Field (NeRF) that could renderseasonal features independently of viewing angle and solar angle while stillbeing able to render shadows. We teach our network to render seasonal featuresby introducing one more input variable -- time of the year. However, the smalltraining datasets typical of satellite imagery can introduce ambiguities incases where shadows are present in the same location for every image of aparticular season. We add additional terms to the loss function to discouragethe network from using seasonal features for accounting for shadows. We showthe performance of our network on eight Areas of Interest containing imagescaptured by the Maxar WorldView-3 satellite. This evaluation includes testsmeasuring the ability of our framework to accurately render novel views,generate height maps, predict shadows, and specify seasonal featuresindependently from shadows. Our ablation studies justify the choices made fornetwork design parameters.

Incorporating Season and Solar Specificity into Renderings made by a NeRF Architecture using Satellite Images