  We explore simple methods for adapting a trained multi-task UNet which
predicts canopy cover and height to a new geographic setting using remotely
sensed data without the need of training a domain-adaptive classifier and
extensive fine-tuning. Extending previous research, we followed a selective
alignment process to identify similar images in the two geographical domains
and then tested an array of data-based unsupervised domain adaptation
approaches in a zero-shot setting as well as with a small amount of
fine-tuning. We find that the selective aligned data-based image matching
methods produce promising results in a zero-shot setting, and even more so with
a small amount of fine-tuning. These methods outperform both an untransformed
baseline and a popular data-based image-to-image translation model. The best
performing methods were pixel distribution adaptation and fourier domain
adaptation on the canopy cover and height tasks respectively.
