![U-Net for Agricultural Field Segmentation [P]](https://preview.redd.it/2u0vgg3tpeyg1.png?width=140&height=140&crop=1:1,smart&auto=webp&s=b72cba100978e7aac89c442908632ce378e459f8) | Hi everyone, I’m working on a solo student project (it was supposed to be a team of five, but here I am) focused on agricultural field analytics. The problem: When I switch to raw Sentinel-2 data, the model’s confidence drops to almost zero. Questions: Any advice on making the model more robust for real-world conditions would be appreciated. P.S. I’ve been coding for the last 12 hours and have already started drinking just to avoid looking at this mess again, so I might have missed some community rules. If needed, I can share the full code , it’s all public. Training: Real: [link] [comments] |
U-Net for Agricultural Field Segmentation [P]
Reddit r/MachineLearning / 5/1/2026
💬 OpinionIdeas & Deep AnalysisTools & Practical Usage
Key Points
- A student project is using a U-Net with an attention mechanism to segment agricultural fields, trained on the AI4Boundaries dataset with 5 input channels.
- The model performs poorly when applied to raw Sentinel-2 imagery, with its confidence dropping to near zero.
- The author asks whether stacking multi-date images could reduce noise and cloud interference.
- They also seek advice on handling sun/viewing angle variations not present in the training data and on improving robustness when real-world data differs significantly from the training set.
- The post invites community guidance and offers to share the full public code if needed.
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