Spatiotemporal Classification with limited labels using Constrained Clustering for large datasets
This work addresses the challenge of leveraging few labels for better representation learning in large datasets, specifically for ecological and sustainability applications, though it appears incremental as it builds on existing clustering and representation learning techniques.
The paper tackles the problem of analyzing large unstructured datasets with limited labels by proposing a spatiotemporal clustering method with constrained loss, showing it improves representation separability and enables picking new labeled samples to augment supervised classification on the ReaLSAT dataset of 680,000 lakes.
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.