High-Resolution (0.9 m) terrace mapping in Low-Latitude hilly regions using deep learning and Area–Slope Denoising: A case study from Guangdong Province, China

• First high-resolution (0.9 m) terrace map for low-latitude hilly regions. • Deep learning with slope–area denoising achieves high accuracy and efficiency. • Terraces in Guangdong are small (4725 m 2 ) and gently sloped (8.85°) • Major terrace clusters found in northern Guangdong and Pearl River Delta areas. • Dataset supports agriculture, soil conservation, ecological and rural planning. Terrace construction is a crucial human intervention for improving slope productivity and preventing soil erosion, especially in low-latitude hilly regions with heavy and concentrated rainfall. However, existing terrace mapping methods perform poorly in such areas. In this study, we generated the first high-resolution (0.9 m) terrace distribution map for low-latitude hilly regions by integrating deep learning with slope-area threshold denoising. Using Guangdong Province as a case study, we combined Google Earth imagery, SRTM DEM, and Global 10-m land-cover data. The results showed that the optimal 0.9 m resolution achieved 93.34 % overall accuracy (OA), 79.18 % F1-score, and 65.53 % intersection over union (IoU) on our Guangdong terrace dataset, processing 400 km 2 in 89 min, showing an excellent accuracy–speed trade-off. Statistical analysis revealed that terraces in Guangdong are predominantly small (4725 m 2 on average) and gently sloped (8.85°). Provincial validation confirmed superior performance, with a producer’s accuracy of 80.38 %. Spatially, terraces are mainly clustered in inland hilly areas, especially in northern Guangdong, with additional clusters in the peripheral zones of the Pearl River Delta and in the western and eastern regions. Cities with the largest terrace coverage are Qingyuan (801.98 km 2 ), Zhaoqing (733.11 km 2 ), and Shaoguan (701.53 km 2 ). This high-precision dataset supports agricultural management, soil erosion risk assessment, ecological conservation, and rural revitalisation, while also offering a transferable framework for mapping terrain features in complex landscapes worldwide using remote sensing imagery.