---
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) — [arXiv:2209.03726](https://arxiv.org/abs/2209.03726)

---

## 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 |

**Note on training data and licensing.** Checkpoints fine-tuned on the
Google subset of BDAPPV (`*_google_*`) derive from imagery distributed
under CC-BY-NC 4.0; commercial users should prefer the IGN-trained
checkpoints (`*_ign_*`, CC-BY 4.0 imagery) or assess accordingly. All
models are initialized from Mayer et al. (2022) checkpoints — see their
repository for base model licensing.

---

## 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)

| Train | Test | IoU | F1 | n (test) |
|-------|------|-----|----|----------|
| Google | Google | 0.884 | 0.937 | 1,935 |
| IGN | IGN | 0.735 | 0.844 | 1,239 |
| Google | IGN | 0.561 | 0.709 | 1,239 |
| IGN | Google | 0.657 | 0.786 | 1,935 |

### Classification (InceptionV3)

| Train | Test | Accuracy | Precision | Recall | F1 | n (test) |
|-------|------|----------|-----------|--------|----|----------|
| Google | Google | 0.952 | 0.990 | 0.912 | 0.949 | 3,884 |
| IGN | IGN | 0.640 | 0.831 | 0.309 | 0.451 | 2,593 |
| Google | IGN | 0.592 | 0.815 | 0.188 | 0.306 | 2,593 |
| IGN | Google | 0.543 | 1.000 | 0.083 | 0.153 | 3,884 |

**Note on classification cross-provider results:** the IGN-trained model collapses on Google imagery (Recall=0.08, Precision=1.0), indicating the model rarely predicts positives — a degenerate operating point. This illustrates the severity of the distribution shift documented in [Kasmi et al. (2025)](https://doi.org/10.1017/eds.2025.13).

---

## 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)*