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presentation/tutorial-new-tutorial-group-1.qmd

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## Problem Setting
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- Tutorial Task: **Why rooftop segmentation matters** (solar planning, urban policy)
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- Cities need accurate rooftop maps to plan and scale **solar PV installations**
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- Manual rooftop labeling is **slow** and **costly**
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- Every city looks different → traditional models do not **generalize** well
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- Cities need rooftop maps for solar PV planning
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### Idea:
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- Manual labeling is expensive
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- **Few-shot learning** makes segmentation possible with only a handful of labeled examples
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- Few-shot learning reduces annotation cost
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![](figures/picture_use_case.png){width=90% style="margin-left: 30%;"}
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- Geneva dataset comes in (example for cities)
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## Dataset: [Geneva](https://huggingface.co/datasets/raphaelattias/overfitteam-geneva-satellite-images)
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- **Satellite Images**: High-resolution RGB satellite images of Geneva
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- **Satellite Images**: High-resolution RGB satellite images of Geneva available on Huggingface
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- **Size**: 1,050 labeled image-mask pairs
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- **Task**: Binary segmentation masks (rooftop vs background)
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- **Geographic splits**: 3 grids/ neighborhoods (1301_11, 1301_13, 1301_31)
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- **Image size**: 250x250 pixels
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- **Categories**: Industrial, Residential
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- **Switzerland Segmentation Labels**: Binary masks indicating rooftop locations
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- **Resolution**: Images at various resolutions suitable for few-shot learning
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![](grids_animation.gif){width=100%}
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![](figures/grids_animation.gif){width=80% style="margin-left: 30%;"}
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- Train/test sizes (val?)
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- Example image + mask
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- Overlay visualization (the ones you already generated!)
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- Challenges: small rooftops, shadows, label noise, class imbalance
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**Insert & dont forget**: visuals notebook data preprocessing etc
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## Model & Methods
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- Data Preprocessing
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## Prototypical Networks
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![](figures/illustration_prototypical_network.png){width=100%}
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- high-level schematic (support → prototype → similarity → segmentation)
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