Synthetic aperture radar (SAR) imaging is a type of active remote sensing in which a satellite sends microwave radar wave pulses down to the Earth’s surface. A SAR image is created by processing these pulses over time and space, with each pixel representing the superposition of multiple radar scatters. They produce visual that is sometimes contradictory and incompatible with computer vision systems. Berkeley AI Research developed an initial set of algorithms and models that learned robust representations for RGB, SAR, and co-registered RGB + SAR imagery. Imagery analysts can now use RGB or SAR imagery interchangeably for downstream tasks such as picture classification, semantic segmentation, object detection, and change detection. The researchers used the publicly available BigEarthNet-MM dataset. . . .