Self-Supervised Learning

Self-Supervised Learning (SSL) is a training paradigm where neural networks learn meaningful data representations without human-annotated labels by defining and solving auxiliary pretext tasks derived from the data itself. Common pretext tasks include predicting masked portions of an image, identifying whether two views of the same scene are related, or predicting the relative position of image patches. The representations learned through these tasks capture fundamental data structure and transfer effectively to downstream tasks with limited labeled data, bridging the gap between unsupervised and supervised learning. Self-Supervised Learning for Earth Observation DataSSL is particularly valuable for geospatial AI because satellite imagerySatellite ImagerySatellite imagery consists of photographs and data captured by Earth observation satellites orbiting the planet. Thes... is abundant but labeled datasets are scarce and expensive. Models pretrained with SSL on millions of unlabeled Sentinel-2 or Landsat scenes learn to understand spectral signatures, spatial textures, and seasonal patterns without any human annotation. Masked image modeling teaches models to reconstruct hidden portions of satellite images, building understanding of land cover continuity and spatial context. Temporal SSL objectives that predict future or missing temporal observations capture phenological cycles and change dynamics. Multi-spectral SSL learns relationships between different wavelength bands that are informative for vegetation health, water quality, and soil composition. Benefits and Current State of the ArtSSL dramatically reduces dependence on labeled training data, which is the primary bottleneck in geospatial AI development. Models pretrained with SSL consistently outperform those trained from scratch when fine-tuned with limited labels, especially for rare or under-represented land cover classes. Major Earth observation foundation models from NASA, ESA, IBM, and others are built on SSL pretraining. The approach enables a single pretrained model to support diverse downstream tasks from classification to segmentation to change detectionChange DetectionChange detection uses geospatial data and imagery to track and analyze alterations in landscapes, infrastructure, or ... through task-specific fine-tuning.