1. Overview This dataset contains manually labelled sea-ice masks for a set of Sentinel-1 Extra-Wide (EW) swath scenes acquired in the Canadian Arctic during 2022–2023.Labels were produced for use in sea-ice segmentation experiments comparing supervised UNet models, a BYOL-pretrained UNet, Random Forest classification, and the Segment Anything Model (SAM). The masks provide pixel-level classification of: 2 = Sea ice 3 = Land These labels can be used directly for training and validation of machine-learning models, or as a reference dataset for benchmarking segmentation methods and lead-detection approaches. 2. Source Imagery The labels correspond to Sentinel-1 SAR scenes (HH and HV, EW mode) processed through ESA SNAP.Raw satellite images are not included here due to ESA licensing restrictions. They can be downloaded from: Copernicus Browserhttps://browser.dataspace.copernicus.eu/ Each mask is named according to the corresponding Sentinel-1 SAFE product. 3. Preprocessing (Summary) All SAR scenes were processed in ESA SNAP and exported as terrain-corrected, georeferenced GeoTIFFs.The pixel spacing is approximately 80 m, consistent with EW mode multilooking and terrain correction. 4. Labelling Procedure Labels were digitised manually in QGIS using processed HH + HV composites as visual guidance. Digitisation performed per scene. Visual interpretation based on SAR backscatter texture, tone, and contextual patterns. Land pixels were labelled explicitly as 3. Output resolution matches the SAR grid (∼80 m). 5. File Contents Each labelled scene is provided as: Sentinel_1_file_name_labels.tif A GeoTIFF containing: 2 = Sea ice 3 = Land All files include georeferencing information and match the spatial extent and resolution of the corresponding Sentinel-1 scene. 6. Coordinate Reference System All masks use the Arctic polar stereographic projection: EPSG:3995 Datum: WGS84 Units: metres Pixel size: ~80 × 80 m 7. Usage Notes The dataset is suitable for semantic segmentation, active learning, lead detection, and self-supervised SAR benchmarking. If any scene contains NoData areas (e.g., sensor padding or terrain-correction gaps), these are encoded as -9999 and should be excluded in training pipelines. Sea ice is consistently labelled as 2, enabling straightforward class-mapping across experiments. 8. Citation If you use this dataset, please cite the Zenodo DOI and the associated research article (when available): Seston, J., Harcourt, W.D., Leontidis, G., Rea, B., Spagnolo, M., & McWhinnie, L. (2025).Manually Labelled Sea Ice Masks for Sentinel-1 SAR Imagery in the Canadian Arctic (2022–2023).Zenodo. 9. Contact For questions about the dataset or related research: Jacob SestonSchool of Geosciences, University of AberdeenEmail: j.seston.23@abdn.ac.uk