Software

DeepPVMapper

DeepPVMapper is a deep learning-based mapping algorithm developped to map rooftop PV installations over France.

DeepPVMapper
Flowchart of DeepPVMapper.

The Github repository is accessible here. Click here DOI to download the minimum data (images, model weights, additional data) to replicate the example provided in the Github repository.

The list of publications associated to the paper are accessible here:

PyPVRoof

PyPVRoof is a Python library for extracting characteristics of rooftop PV systems using a geolocalized polygon and additional data (3D LiDAR data, additional registry).

PyPVRoof
Flowchart of PyPVRoof.

Photovoltaic (PV) energy grows at an unprecedented pace, which makes it difficult to maintain up-to-date and accurate PV registries, which are critical for many applications such as PV power generation estimation. This lack of qualitative data is especially true in the case of rooftop PV installations. As a result, extensive efforts are put into the constitution of PV inventories. However, although valuable, these registries cannot be directly used for monitoring the deployment of PV or estimating the PV power generation, as these tasks usually require PV systems {\it characteristics}. To seamlessly extract these characteristics from the global inventories, PyPVRoof. PyPVRoof is a Python package to extract essential PV installation characteristics. These characteristics are tilt angle, azimuth, surface, localization, and installed capacity. PyPVRoof is designed to cover all use cases regarding data availability and user needs and is based on a benchmark of the best existing methods. Data for replicating our accuracy benchmarks are available on our Zenodo repository, DOI and the package code is accessible on our Github repository.

The supporting preprint is accessible here: