A High-Resolution Multi-Temporal Remote Sensing Dataset for Levee-like Feature Segmentation in Arid Regions

Levee-like features are critical for water regulation in arid regions, but their automated extraction from remote sensing imagery remains challenging due to the scarcity of high-resolution labeled datasets. This data descriptor introduces a high-resolution remote sensing image dataset for semantic segmentation of levee-like features. The dataset covers 11 regions across Xinjiang and Gansu Province in northwestern China. It includes 459 single-phase base images with a spatial resolution of 0.50 m, as well as multi-temporal images of the same regions captured at different times. All annotations were manually drawn in polygon mode using the LabelMe tool and converted into YOLO format label files. The dataset adopts a strict strategy to prevent data leakage: first, training, validation and test sets are divided based on single-phase images, and then multi-temporal images are allocated to the corresponding data subsets according to their spatial locations. The dataset has been publicly released on the ScienceDB platform under the CC BY 4.0 license. YOLO and U-Net segmentation experiments on the test set achieved promising results, demonstrating its usability for levee-like feature segmentation. This dataset can provide fundamental data support for research on levee-like feature extraction, remote sensing change detection, and cross-region model transfer learning.