  Satellite image time series (SITS) segmentation is crucial for many
applications like environmental monitoring, land cover mapping and agricultural
crop type classification. However, training models for SITS segmentation
remains a challenging task due to the lack of abundant training data, which
requires fine grained annotation. We propose S4 a new self-supervised
pre-training approach that significantly reduces the requirement for labeled
training data by utilizing two new insights: (a) Satellites capture images in
different parts of the spectrum such as radio frequencies, and visible
frequencies. (b) Satellite imagery is geo-registered allowing for fine-grained
spatial alignment. We use these insights to formulate pre-training tasks in S4.
We also curate m2s2-SITS, a large-scale dataset of unlabeled,
spatially-aligned, multi-modal and geographic specific SITS that serves as
representative pre-training data for S4. Finally, we evaluate S4 on multiple
SITS segmentation datasets and demonstrate its efficacy against competing
baselines while using limited labeled data.
