Zhehuan Cao, Berhanu Fiseha, Ping Tesema, Jianfeng Fu, Ahmed Ren, Nasr, Ping Fu, Tesema, Ahmed Nasr
We lifted 6 functions out of this paper's own repositories and ran 3 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| Lyra-alpha/MCD-Net | — | 3 of 6 |
| Function | Status | Where it lives |
|---|---|---|
| ASPP | Ran | Lyra-alpha/MCD-Net/nets/deeplabv3_plus.py code served (permissive licence) · get_code("64b6f302e40a5c20") |
| CBAM | Ran | Lyra-alpha/MCD-Net/nets/deeplabv3_plus.py code served (permissive licence) · get_code("43ecc7d8039d0bd5") |
| load_url | Ran | Lyra-alpha/MCD-Net/nets/deeplabv3_plus.py code served (permissive licence) · get_code("0896d8636b35a30b") |
| MCDNet | Not yet run | Lyra-alpha/MCD-Net/nets/deeplabv3_plus.py code served (permissive licence) · get_code("79e891c2c4f53789") |
| MobileNetV2 | Not yet run | Lyra-alpha/MCD-Net/nets/deeplabv3_plus.py code served (permissive licence) · get_code("8737da69b887b3fc") |
| mobilenetv2 | Not yet run | Lyra-alpha/MCD-Net/nets/deeplabv3_plus.py code served (permissive licence) · get_code("b1d54966338738d2") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Glacial segmentation is essential for reconstructing past glacier dynamics and evaluating climatedriven 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 highaltitude glacial monitoring.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2601.02091")
get_code_for_paper("2601.02091")
have("2601.02091")
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