Guest Editor: WANG Zhirui (Key Laboratory of Target Cognition and Application Technology (TCAT), Aerospace Information Research Institute, Chinese Academy of Sciences)

The AIR-PolSAR-Seg-2.0 dataset aims to build a polarimetric SAR land cover classification dataset for large-scale complex scenes, promoting research and development in polarimetric synthetic aperture radar land cover classification within the field of SAR image intelligent interpretation. The dataset is composed of GF-3 L1A complex SAR images from three different regions, with a spatial resolution of 8 m and includes four polarimetric modes (HH, HV, VH, and VV), covering six typical land cover categories: water bodies, vegetation, bare land, buildings, roads, and mountains. The dataset exhibits characteristics of complex scenes, large scale, diverse scattering intensities, irregular boundary distributions, varying category scales, and unbalanced sample distribution. Additionally, the complete SAR images of the three scenes have been cropped into 24,672 slices of 512×512 pixels to facilitate experimental verification within a deep learning framework.