TY  - Data
T1  - AI-Ready Standardized Semantic Segmentation Dataset for the silted land formed by check dams in the Loess Plateau
A1  - LIU Jingqi
A1  - HUANG Bo
A1  - Zhang Yaonan
A1  - MIN Yufang
DO  - 10.12072/ncdc.dpsl.db6865.2025
PY  - 2025
DA  - 2025-06-03
PB  - National Cryosphere Desert Data Center
AB  - =As a key water and soil conservation project in the Loess Plateau, check dams have core functions of controlling soil erosion and ensuring food security. However, their intelligent management has long been limited by low data acquisition efficiency, insufficient model generalization ability, and lack of standardized data sets. Technical bottleneck. This study relies on the 0.75 m high-resolution remote sensing image of Jilin-1 and takes Jiuyuangou, a typical basin of the Loess Plateau as the sampling area, and builds a set of AI-Ready standardized semantic segmentation data set for dam sites on the Loess Plateau. Through a systematic sample preparation process, this dataset covers key aspects such as grid division, sample screening, pixel-level semantic annotation and image enhancement, forming a high-precision dataset containing 2920 samples, effectively ensuring the integrity of data space representation and algorithm generalization ability requirements. Data quality assessment experiments show that models such as DenseUnet trained based on this dataset perform well on the verification set, with the average cross-to-merge ratio (mIoU) exceeding 80%, and the overall accuracy (OA) reaching more than 89%. Compared with the public dataset, the spatial matching and reliability of the dam body extraction results are significantly improved. This dataset realizes the refined semantic distinction between dam bodies and background in complex landform environments, fills the gap in standardized datasets in the field of intelligent identification of dams. It not only provides high-precision spatio-temporal mapping of check dams, dynamic assessment of dam failure risks, and water and soil conservation. Quantitative research on effects provides key data support, and also opens up a new technical path for artificial intelligence-driven optimization of soil and water conservation projects. It has important practical value for promoting ecological protection and high-quality deve
DB  - NCDC
UR  - http://www.ncdc.ac.cn/portal/metadata/7dd767bb-1be8-414e-a800-3a4291bfd39d
ER  -