Many Antarctic ice shelves have undergone accelerated thinning and retreat in recent decades, weakening their buttressing effect on grounded ice and increasing the risk of further sea-level rise. Surface damage, including crevasses, rifts and heavily fractured areas, is an important indicator of ice shelf structural integrity, but there is limited understanding of its long-term evolution across Antarctic ice shelves. Here we present a new surface damage dataset for nine representative Antarctic ice shelves, derived from Landsat optical imagery covering the period 1999–2024. These ice shelves include Amery, Brunt, Crosson, Dotson, Holmes, Larsen B, Pine Island, Thwaites and Totten, and encompass a range of change behaviours from relatively stable to rapidly changing systems. A deep-learning image segmentation model was trained on a manually annotated dataset from diverse Antarctic ice shelves to automatically map surface damage. To extend the usable record, Landsat 7 scan-line-corrector-off imagery was restored using a diffusion-model-based framework fine-tuned for Antarctic imagery. The final dataset contains 170 surface damage maps at 30 m resolution, each representing a single ice shelf for a specific year. Temporal coverage varies among ice shelves owing to differences in the availability of usable imagery. The model achieved a mean intersection over union of 0.845 on the test set and 0.822 on an independent validation ice shelf not included in model training. The dataset demonstrates good multi-temporal consistency, supporting its use for time-series analysis. Among the nine ice shelves, Pine Island, Thwaites and Larsen B show the most pronounced surface damage changes during the study period. Compared with existing studies, this dataset provides improved temporal continuity over multi-decadal timescales at substantially finer spatial resolution, offering new insights into the long-term evolution of Antarctic ice shelves and contributing to a better understanding of ice shelf instability. The dataset is publicly available at <a href="https://doi.org/10.5281/zenodo.20425951" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.20425951</a> (Tang et al., 2026a).