---
license: cc-by-4.0
tags:
  - image-segmentation
  - image-classification
  - solar-panels
  - photovoltaic
  - remote-sensing
  - aerial-imagery
  - pytorch
datasets:
  - gabrielkasmi/bdappv
---

# BDAPPV Models

Baseline models for the [BDAPPV dataset](https://huggingface.co/datasets/gabrielkasmi/bdappv) — aerial images of rooftop photovoltaic installations in France and Belgium.

**Paper:** [Kasmi et al., Scientific Data, 2023](https://doi.org/10.1038/s41597-023-01951-4)

---

## Models

Two architectures, trained independently on each imagery provider:

| File | Task | Architecture | Provider |
|------|------|-------------|----------|
| `deeplab_google_best.pth` | Segmentation | DeepLabV3-ResNet101 | Google |
| `deeplab_ign_best.pth` | Segmentation | DeepLabV3-ResNet101 | IGN |
| `inception_google_best.pth` | Classification | InceptionV3 | Google |
| `inception_ign_best.pth` | Classification | InceptionV3 | IGN |

---

## Benchmark protocol

Three evaluation tracks are defined:

**Track 1 — Segmentation (single provider)**
Train and evaluate on the same provider. Report IoU and F1 on the test split.

**Track 2 — Classification (single provider)**
Train and evaluate on the same provider. Report accuracy and F1 on the test split.

**Track 3 — Distribution shift (cross-provider)**
Train on Google, evaluate on IGN test split. This is the primary robustness benchmark. Report IoU.

Rules:
- The test split must not be used for model selection or hyperparameter tuning — validation split only.
- The spatial holdout by department must not be modified. Re-splitting invalidates comparability with published results.
- For Track 3, only the Google training split may be used for training.

---

## Results

Models evaluated on the official test split (spatial holdout by French department — see dataset card for details).

### Segmentation (DeepLabV3-ResNet101)

| Provider | IoU | F1 |
|----------|-----|----|
| Google | TBD | TBD |
| IGN | TBD | TBD |

### Classification (InceptionV3)

| Provider | Accuracy | F1 |
|----------|----------|----|
| Google | TBD | TBD |
| IGN | TBD | TBD |

### Distribution shift benchmark

Train on Google, evaluate on IGN — the intended cross-provider protocol:

| Model | Train | Test | IoU |
|-------|-------|------|-----|
| DeepLabV3-ResNet101 | Google | IGN | TBD |

---

## Usage

A `model.py` helper is included in this repo to simplify loading:

```python
from huggingface_hub import hf_hub_download
import importlib.util

path = hf_hub_download("gabrielkasmi/bdappv-models", "model.py")
spec = importlib.util.spec_from_file_location("bdappv_model", path)
mod  = importlib.util.module_from_spec(spec)
spec.loader.exec_module(mod)

seg = mod.load_segmentation_model("google")    # or "ign"
clf = mod.load_classification_model("google")  # or "ign"
```

Both functions return the model in `eval()` mode. An optional `device` argument is supported (`"cpu"`, `"cuda"`, `"mps"`).

---

## Training

Models trained on the official BDAPPV splits using:

- **Optimizer:** AdamW (lr=1e-4, weight_decay=1e-4)
- **Scheduler:** Cosine annealing
- **Effective batch size:** 32 (batch 16 × grad accum 2)
- **Early stopping:** patience=7 epochs on validation metric
- **Input size:** 400×400 px
- **Initialization:** checkpoints from [Mayer et al. (2022)](https://doi.org/10.1016/j.apenergy.2021.118469), who fine-tuned DeepLabV3-ResNet101 and InceptionV3 on 10 cm/px orthoimagery from North Rhine-Westphalia (Germany) for rooftop PV detection. These checkpoints were then further fine-tuned on BDAPPV using the splits above.

Training scripts available in the [BDAPPV dataset repository](https://huggingface.co/datasets/gabrielkasmi/bdappv).

---

## Citation

If you use these models, please cite:

```bibtex
@article{kasmi2022towards,
  title={Towards unsupervised assessment with open-source data of the accuracy of deep learning-based distributed PV mapping},
  author={Kasmi, Gabriel and Dubus, Laurent and Blanc, Philippe and Saint-Drenan, Yves-Marie},
  journal={arXiv preprint arXiv:2207.07466},
  year={2022}
}
```

## References

- Mayer et al. (2022). [3D-PV-Locator: Large-scale detection of rooftop-mounted photovoltaic systems in 3D.](https://doi.org/10.1016/j.apenergy.2021.118469) *Applied Energy*, 310, 118469. *(source of the base checkpoints)*
- Kasmi et al. (2023). [A crowdsourced dataset of aerial images with annotated solar photovoltaic arrays and installation metadata.](https://doi.org/10.1038/s41597-023-01951-4) *Scientific Data*, 10, 59. *(BDAPPV dataset)*
- Kasmi et al. (2025). [Space-scale exploration of the poor reliability of deep learning models: the case of the remote sensing of rooftop photovoltaic systems.](https://doi.org/10.1017/eds.2025.13) *Environmental Data Science*. *(cross-provider distribution shift)*