<p>Efficient segmentation of oiled pixels in optical remotely sensed images is the precondition of optical identification and classification of different spilled oils, which remains one of the keys to optical remote sensing of oil spills. Optical remotely sensed images of oil spills are inherently multidimensional and embedded with a complex knowledge framework. This complexity often hinders the effectiveness of mechanistic algorithms across varied scenarios. Although optical remote-sensing theory for oil spills has advanced, the scarcity of curated datasets and the difficulty of collecting them limit their usefulness for training deep learning models. This study introduces a data expansion strategy that utilizes the Segment Anything Model (SAM), effectively bridging the gap between traditional mechanism algorithms and emergent self-adaptive deep learning models. Optical dimension reduction is achieved through standardized preprocessing processes that address the decipherable properties of the input image. After preprocessing, SAM can swiftly and accurately segment spilled oil in images. The unified AI-based workflow significantly accelerates labeled-dataset creation and has proven effective for both rapid emergency intelligence during spill incidents and the rapid mapping and classification of oil footprints across China’s coastal waters. Our results show that coupling a remote sensing mechanism with a foundation model enables near-real-time, large-scale monitoring of complex surface slicks and offers guidance for the next generation of detection and quantification algorithms.</p>