Contrastive Learning

Contrastive Learning is a machine learning approach that trains neural networks to produce similar representations for semantically related inputs and dissimilar representations for unrelated inputs. The model processes pairs or groups of examples, learning an embedding space where positive pairs (different views of the same concept) are pulled close together while negative pairs (unrelated examples) are pushed apart. Loss functions like InfoNCE and NT-Xent quantify this objective, and the resulting representations capture meaningful semantic structure without requiring explicit labels. Contrastive Learning for Satellite ImageryContrastive learning is naturally suited to geospatial dataGeospatial DataGeospatial data encompasses information about the location, shape, and relationships of physical features on Earth. I..., which provides multiple complementary views of the same locations. Spatial augmentations create positive pairs from different crops or transformations of the same satellite image tile. Temporal pairs treat images of the same location at different dates as positive examples, teaching models that seasonal appearance changes do not alter the underlying land cover identity. Multi-spectral contrasting pairs optical and SARSARSynthetic Aperture Radar (SAR) is an active remote sensing technology that uses microwave radar pulses to create high... views of the same area, learning to align information across sensor modalities. Geographic negative sampling ensures that negative pairs come from genuinely different locations, strengthening the learned representations. Frameworks and Geospatial ApplicationsProminent contrastive learning frameworks include SimCLR, MoCo, BYOL, and DINO, each with variations in how positive and negative pairs are constructed and compared. In geospatial applications, contrastive pretraining produces feature extractors that transfer well to land cover classificationLand Cover ClassificationLand cover classification is the process of categorizing Earth's surface into distinct classes such as forest, cropla..., change detectionChange DetectionChange detection uses geospatial data and imagery to track and analyze alterations in landscapes, infrastructure, or ..., and scene retrieval with minimal labeled data. Geo-aware contrastive learning that incorporates geographic coordinates as a learning signal has shown improvements over purely visual approaches. The approach is particularly effective when combined with the massive archives of multi-temporal satellite imagerySatellite ImagerySatellite imagery consists of photographs and data captured by Earth observation satellites orbiting the planet. Thes... available from programs like Copernicus and Landsat.