  Creating separable representations via representation learning and clustering
is critical in analyzing large unstructured datasets with only a few labels.
Separable representations can lead to supervised models with better
classification capabilities and additionally aid in generating new labeled
samples. Most unsupervised and semisupervised methods to analyze large datasets
do not leverage the existing small amounts of labels to get better
representations. In this paper, we propose a spatiotemporal clustering paradigm
that uses spatial and temporal features combined with a constrained loss to
produce separable representations. We show the working of this method on the
newly published dataset ReaLSAT, a dataset of surface water dynamics for over
680,000 lakes across the world, making it an essential dataset in terms of
ecology and sustainability. Using this large unlabelled dataset, we first show
how a spatiotemporal representation is better compared to just spatial or
temporal representation. We then show how we can learn even better
representation using a constrained loss with few labels. We conclude by showing
how our method, using few labels, can pick out new labeled samples from the
unlabeled data, which can be used to augment supervised methods leading to
better classification.
