PipelineText → ImageMid creditEN

End-to-End Segmentation Training Pipeline

Five-stage horizontal pipeline from raw satellite tiles to pixel-wise predictions.

When to use this prompt

For methodology sections of remote sensing, medical imaging, or any segmentation paper.

The prompt

End-to-end training pipeline for a semantic segmentation model on satellite imagery. Five stages connected left-to-right by labeled black arrows on a white background.

Stage 1 — Input
Surface reflectance Landsat tiles (atmospherically corrected). Show a 2x2 grid of tile thumbnails inside the stage.

Stage 2 — Pre-processing
- Remove zero-value pixels.
- Slice large scenes into 512x512 tiles.
- Normalize per channel.

Stage 3 — Splits
Train (80%) / Val (10%) / Test (10%). Show as a horizontal stacked bar with each split labeled.

Stage 4 — Model
U-Net backbone with encoder-decoder skip connections. Show the U-shape inside this stage.

Stage 5 — Output
Per-pixel class probabilities -> argmax -> segmentation map. Show a small example mask thumbnail.

Style: clean rounded rectangle boxes, minimal palette (navy, teal, gray), sans-serif labels, no decorative icons. IEEE / Remote Sensing journal style.

Variations

Top-down, single-column variant

Same pipeline content, but laid out top-to-bottom in a single column, suitable for a vertical column figure in a 2-column paper. Each stage is a row; arrows are short vertical chevrons.

With augmentation branch

Add a sixth side branch between Stage 2 and Stage 3 labeled "Augmentation" containing rotation, flip, color jitter, and CutMix. Show the branch feeding back into the train split only.

Tips

  • Number every stage. Numbered stages reduce label drift.
  • List 2–4 bullets inside each stage — more than that gets truncated visually.
  • Specify the exact thumbnail you want inside each stage (e.g. "show a 2x2 grid of tile thumbnails").

FAQ

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