Daniel G. Silva

iSAGE: A Human-in-the-Loop Framework for Remote Sensing Semantic Segmentation via Sparse Point Supervision

Training semantic segmentation models is especially costly in remote sensing, since most problems require a new dataset as targets vary with resolution, sensors, and region. Approaches to reduce this cost are increasingly common, but most add machinery that expands few labeled pi...

Remote SAMsing: From segment anything to segment everything

The Segment Anything Model 2 (SAM2) produces high-quality zero-shot segmentation of natural images, but two obstacles block its use at scale for remote sensing. First, the automatic mask generator trades coverage against quality: strict acceptance thresholds leave most of a scene...