Glacial segmentation is essential for reconstructing past glacier dynamics and evaluating climate-driven landscape change. However, weak optical contrast and the limited availability of high-resolution DEMs hinder automated mapping. This study introduces the first large-scale optical-only moraine segmentation dataset, comprising 3,340 manually annotated high-resolution images from Google Earth covering glaciated regions of Sichuan and Yunnan, China. We develop MCD-Net, a lightweight baseline that integrates a MobileNetV2 encoder, a Convolutional Block Attention Module (CBAM), and a DeepLabV3+ decoder. Benchmarking against deeper backbones (ResNet152, Xception) shows that MCD-Net achieves 62.3\% mean Intersection over Union (mIoU) and 72.8\% Dice coefficient while reducing computational cost by more than 60\%. Although ridge delineation remains constrained by sub-pixel width and spectral ambiguity, the results demonstrate that optical imagery alone can provide reliable moraine-body segmentation. The dataset and code are publicly available at https://github.com/Lyra-alpha/MCD-Net, establishing a reproducible benchmark for moraine-specific segmentation and offering a deployable baseline for high-altitude glacial monitoring.

翻译：冰川分割对于重建历史冰川动态和评估气候驱动的景观变化至关重要。然而，微弱的光学对比度以及高分辨率数字高程模型（DEM）的有限可用性阻碍了自动化制图。本研究引入了首个大规模纯光学冰碛物分割数据集，包含来自Google Earth的3,340张人工标注的高分辨率图像，覆盖中国四川和云南的冰川区域。我们开发了MCD-Net，一种轻量级基准模型，它集成了MobileNetV2编码器、卷积块注意力模块（CBAM）以及DeepLabV3+解码器。与更深层骨干网络（ResNet152、Xception）的基准测试表明，MCD-Net在降低超过60%计算成本的同时，实现了62.3%的平均交并比（mIoU）和72.8%的Dice系数。尽管山脊线描绘仍受限于亚像素宽度和光谱模糊性，但结果表明仅凭光学影像即可提供可靠的冰碛体分割。该数据集与代码已在https://github.com/Lyra-alpha/MCD-Net公开，为冰碛物特异性分割建立了一个可复现的基准，并为高海拔冰川监测提供了一个可部署的基线模型。