Dataset of oil slicks, look-alikes and remarkable SAR signatures obtained from Sentinel-1 data in the Eastern Mediterranean Sea

Abstract. Publicly available datasets for oil spill detection are scarce, making it difficult to compare the performance of different detection algorithms. To address this, this paper introduces a comprehensive labeled dataset of oil slicks, look-alikes, and other remarkable oceanic phenomena, derived from Sentinel-1 Synthetic Aperture Radar (SAR) products in the Eastern Mediterranean Sea in 2019. The dataset contains 3225 oil objects across 1365 image patches, along with an additional 2290 image patches featuring look-alikes or other phenomena. Data are available at https://doi.org/10.1594/PANGAEA.980773 (Yang and Singha, 2025). This dataset enables researchers to evaluate their oil spill detection models and compare performance with other studies. To facilitate this, the performance of an oil spill detector from a previous study on the dataset is provided as a baseline. In addition, to help the researchers better understand what phenomena their object detector might be confusing with oil slicks, the image patches without oil objects were sorted into several subgroups. On the other hand, for researchers looking to apply object detection models to oil slick detection but lacking a starting dataset, this dataset can serve as a valuable training resource. Beyond dataset presentation, this paper also explains the formation of different oceanic phenomena and their SAR signatures, supported by examples and supplementary materials. These insights help researchers from various backgrounds, such as remote sensing, oceanography, and machine learning, better understand the sources of SAR signatures.