  Domain adaptation (DA) strives to mitigate the domain gap between the source
domain where a model is trained, and the target domain where the model is
deployed. When a deep learning model is deployed on an aerial platform, it may
face gradually degrading weather conditions during operation, leading to
widening domain gaps between the training data and the encountered evaluation
data. We synthesize two such gradually worsening weather conditions on real
images from two existing aerial imagery datasets, generating a total of four
benchmark datasets. Under the continual, or test-time adaptation setting, we
evaluate three DA models on our datasets: a baseline standard DA model and two
continual DA models. In such setting, the models can access only one small
portion, or one batch of the target data at a time, and adaptation takes place
continually, and over only one epoch of the data. The combination of the
constraints of continual adaptation, and gradually deteriorating weather
conditions provide the practical DA scenario for aerial deployment. Among the
evaluated models, we consider both convolutional and transformer architectures
for comparison. We discover stability issues during adaptation for existing
buffer-fed continual DA methods, and offer gradient normalization as a simple
solution to curb training instability.
