This repository contains the necessary data and notebooks to replicate the results and analyses carried out in the paper "PyPVRoof: a Python package for the modular extraction of minimal metadata of rooftop PV installations". The project repository is accessible here: https://github.com/gabrielkasmi/pypvroof The paper is accessible here: This repository is self-sufficient to replicate the results. This repository also hosts the data necessary to run the notebook hands-on.ipynb from the public repository of the project. The dataset is organized as follows: characteristics/ root folder for the replication of the results /CSV: contains the `.csv` files necessary to train and evaluate the methods /Data: main folder for the data /BDAPPV: folder containing the data stemming from BDAPPV. /DSFrance: folder containing auxiliary data to run the methods on the outputs of DeepPVMapper /LiDAR: folder containing a sample of LiDAR images for evaluation /Yann: folder containing auxiliary data and annotations. /models: folder containing the weights of the models /notebooks: folder containing the Jupyter notebook in which the methods are evaluated. Characteristics_Extraction_Methods.ipynb is the notebook in which the methods are gathered and defined. The other notebooks can be used to generate the models using the data located in the /Data folder. /scalers: folder containing the scaler for the random forest defined for tilt and azimuth estimation env.yml: use this file to defined a virtual environment from which the notebooks can be launched. README.md: the readme file hands-on/ Root folder to run the hands-on notebook from the repository, accessible here: : https://github.com/gabrielkasmi/pypvroof/blob/master/hands-on.ipynb bdappv-metadata.csv: a file containing ground truth information for installations located in France. This dataset is filtered from the BDAPPV dataset. Localizations were added based on the information on the departement. arrays_69.geojson. A raw file coming from DeepPVMapper' first step (detection and segmentation). This file is the output of the hands-on notebook of DeepPVMapper. You can access the repository here: https://github.com/gabrielkasmi/deeppvmapper lookup-table.json. The lookup table of DeepPVMapper tile.tif: a LiDAR raster as an example for the tilt and azimuth estimation using Theil-Sen algorithm