<p>Addressing the issues of poor timeliness and low spatial resolution in roughness data for micro-siting of wind farms, this study proposes an enhanced U-Net approach integrating channel attention mechanisms with adaptive segmentation techniques. By leveraging channel weight adjustments to reinforce key features and employing overlapping segmentation principles, the U-Net semantic segmentation model is refined. The introduction of the Efficient Channel Attention (ECA) module at critical junctures for feature extraction and large-scale image processing, coupled with an adaptive segmentation mechanism, enhances segmentation accuracy when applying high-resolution UAV remote sensing imagery to complex terrain feature segmentation in micro-siting for wind farms. (Efficient Channel Attention) module is introduced at critical points for feature extraction and large-scale image processing, alongside the design of an adaptive segmentation mechanism. This enables enhanced segmentation accuracy when applying high-resolution UAV remote sensing imagery to complex terrain and feature segmentation in wind farm micro-siting. Experimental results on remote sensing image datasets from Sandu County, Guizhou and Qing County, Hebei demonstrate that the proposed model outperforms the four comparative models across four core metrics: mean intersection over union (mIoU), mean precision, recall, and F1 score. Furthermore, it significantly enhances the integrity of small-scale feature segmentation. The proposed method demonstrates advanced capabilities in efficiently processing ultra-high-resolution imagery and accurately capturing small targets such as rural lanes, offering novel insights for extracting roughness characteristics in micro-siting for wind farms.</p>