  Remote sensing projects typically generate large amounts of imagery that can
be used to train powerful deep neural networks. However, the amount of labeled
images is often small, as remote sensing applications generally require expert
labelers. Thus, semi-supervised learning (SSL), i.e., learning with a small
pool of labeled and a larger pool of unlabeled data, is particularly useful in
this domain. Current SSL approaches generate pseudo-labels from model
predictions for unlabeled samples. As the quality of these pseudo-labels is
crucial for performance, utilizing additional information to improve
pseudo-label quality yields a promising direction. For remote sensing images,
geolocation and recording time are generally available and provide a valuable
source of information as semantic concepts, such as land cover, are highly
dependent on spatiotemporal context, e.g., due to seasonal effects and
vegetation zones. In this paper, we propose to exploit spatiotemporal
metainformation in SSL to improve the quality of pseudo-labels and, therefore,
the final model performance. We show that directly adding the available
metadata to the input of the predictor at test time degenerates the prediction
quality for metadata outside the spatiotemporal distribution of the training
set. Thus, we propose a teacher-student SSL framework where only the teacher
network uses metainformation to improve the quality of pseudo-labels on the
training set. Correspondingly, our student network benefits from the improved
pseudo-labels but does not receive metadata as input, making it invariant to
spatiotemporal shifts at test time. Furthermore, we propose methods for
encoding and injecting spatiotemporal information into the model and introduce
a novel distillation mechanism to enhance the knowledge transfer between
teacher and student. Our framework dubbed Spatiotemporal SSL can be easily
combined with several stat...
