A comprehensive benchmark and dual-encoder baseline for terraced field extraction in complex mountainous regions

Terraces play a vital role in mitigating runoff and intercepting sediment. Large-scale, high-precision extraction of terraces from remote sensing imagery is essential for environmental monitoring and soil erosion control. However, accurate spatial identification of terraces still presents significant challenges, including scale variability, shadow interference, and complex geometric configurations. To address these issues, we propose DETS-Net, a dual-encoder network that integrates CNN and Transformer architectures. The network employs ResNet-34 as the CNN encoder for local feature detail extraction, utilizes a Transformer encoder to strengthen long-range dependency modeling, and incorporates an adaptive feature fusion (AFF) module for the dynamic integration of the two feature branches. We develop the THDIR dataset, which covers four typical terrain types, namely, plateau mountains, hills, and mountain-basin transition zones, and contains 9,336 standardized 256 × 256 pixel images. We conduct systematic comparisons with eight representative deep learning models. Experimental results demonstrate that DETS-Net achieves an overall accuracy (OA) of 89.91%, a mean intersection over union (MIoU) of 81.28%, and an F1-score of 87.99% on the THDIR dataset, outperforming current mainstream methods in segmentation precision. We apply the optimized model to 2023 remote sensing imagery of Luotian County in the Dabie Mountains, Hubei Province, and generate a high-precision county-level terrace spatial distribution map. This framework supports the assessment of soil and water conservation benefits through area statistics, slope distribution data, and high-precision boundary extraction and provides a reproducible technical pathway for automated terrace mapping using high-resolution remote sensing imagery in complex mountainous regions.