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, we introduce {\tt PyPVRoof}.
{\tt PyPVRoof} is a Python package to extract essential PV installation
characteristics. These characteristics are tilt angle, azimuth, surface,
localization, and installed capacity. {\tt 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
\cite{tremenbert2023pypvroof}, and the package code is accessible at this URL:
\url{https://github.com/gabrielkasmi/pypvroof}.Comment: 22 pages, 9 figures, 5 table