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<!doctype html>
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<title>Software | Gabriel Kasmi</title>
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<header>
<h1 class="title">Software</h1>
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<ul>
<li><a href="#DeepPVMapper">DeepPVMapper</a></li>
<li><a href="#PyPVRoof">PyPVRoof</a></li>
<li><a href="index.html">Back to home</a></li>
</ul>
</header>
<section>
<h2 id="DeepPVMapper">DeepPVMapper</h2>
<p>
DeepPVMapper is a deep learning-based mapping algorithm developped to map
rooftop PV installations over France.
</p>
<figure>
<img src="assets/flowchart-dpm.png" alt="DeepPVMapper" style="max-width: 80%; height: auto;">
<figcaption>Flowchart of DeepPVMapper.
</figcaption>
</figure>
<p>
The <a href="https://github.com/gabrielkasmi/deeppvmapper">Github repository</a> is accessible here.
Click here <a href="https://doi.org/10.5281/zenodo.7576814"><img src="https://zenodo.org/badge/DOI/10.5281/zenodo.7576814.svg" alt="DOI"></a>
to download the minimum data (images, model weights, additional data) to replicate the example provided in the
Github repository.
</p>
<p>
The list of publications associated to the paper are accessible here:
<ul>
<li>
Kasmi, G., Dubus, L., Blanc, P., & Saint-Drenan, Y. M. (2022).
Towards unsupervised assessment with open-source data of the accuracy of
deep learning-based distributed PV mapping.
<a href="https://arxiv.org/abs/2207.07466">arXiv preprint arXiv:2207.07466.</a>
</li>
</ul>
</p>
<h2 id="PyPVRoof">PyPVRoof</h2>
<p>
PyPVRoof is a Python library for extracting characteristics of rooftop PV systems using a geolocalized
polygon and additional data (3D LiDAR data, additional registry).
</p>
<figure>
<img src="assets/flowchart-ppr.png" alt="PyPVRoof" style="max-width: 80%; height: auto;">
<figcaption>Flowchart of PyPVRoof.
</figcaption>
</figure>
<p>
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, <a href="https://doi.org/10.5281/zenodo.7586879"><img src="https://zenodo.org/badge/DOI/10.5281/zenodo.7586879.svg" alt="DOI"></a>
and the package code is
accessible on our <a href="https://github.com/gabrielkasmi/pypvroof">Github repository</a>.
</p>
<p>
The supporting preprint is accessible here:
<ul>
<li>
Trémenbert, Y., Kasmi, G., Dubus, L., Saint-Drenan, Y. M., & Blanc, P. (2023).
PyPVRoof: a Python package for extracting the characteristics of rooftop
PV installations using remote sensing data.
<a href="arXiv preprint arXiv:2309.07143.">arXiv preprint arXiv:2309.07143.</a>
</li>
</ul>
</p>
</section>
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