AI RESEARCH
OilSAM2: Memory-Augmented SAM2 for Scalable SAR Oil Spill Detection
arXiv CS.CV
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ArXi:2603.10231v1 Announce Type: new Segmenting oil spills from Synthetic Aperture Radar (SAR) imagery remains challenging due to severe appearance variability, scale heterogeneity, and the absence of temporal continuity in real world monitoring scenarios. While foundation models such as Segment Anything (SAM) enable prompt driven segmentation, existing SAM based approaches operate on single images and cannot effectively reuse information across scenes.