We Mapped ~500k Rooftop PV Installations Across France With Deep Learning — Model, Weights, And Dataset Now Fully Open

**Self-promotion**

Hi r/remotesensing,

I’m sharing DeepPVMapper, an open-source tool we developed to detect and characterize rooftop PV systems from very high-resolution aerial imagery (IGN orthophotos, 20cm).

What’s available:

• Model weights on HuggingFace: huggingface.co/gabrielkasmi/bdappv-models
• Interactive demo (no GPU, ~1 min/km²): huggingface.co/spaces/gabrielkasmi/deeppvmapper
• Training dataset (45k+ images, segmentation masks): huggingface.co/datasets/gabrielkasmi/bdappv
• Full detections for France (~500k systems, GeoJSON): https://zenodo.org/records/19188878
• Code: github.com/gabrielkasmi/deeppvmapper

What it does:
Detects rooftop PV panels and estimates surface area, installed capacity, tilt and azimuth. Deployed at national scale across France — evaluation against official registries (RTE, RNI) revealed 10% missing capacity nationally.

The repo has been refactored and is open to contributions. Happy to discuss methodology, limitations, or potential extensions.

Project page: gabrielkasmi.github.io/deeppvmapper