Segment anything, from space?

Understand

Recently, the first foundation model developed specifically for image segmentation tasks was developed, termed the "Segment Anything Model" (SAM).

• SAM can segment objects in input imagery based on cheap input prompts, such as one (or more) points, a bounding box, or a mask.
• The authors examined the \textit{zero-shot} image segmentation accuracy of SAM on a large number of vision benchmark tasks and found that SAM usually achieved recognition accuracy similar to, or sometimes exceeding, vision models that had been trained on the target tasks.
• The impressive generalization of SAM for segmentation has major implications for vision researchers working on natural imagery.