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).

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), 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.

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. 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. 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. Environmental Data Science. (cross-provider distribution shift)