FRPV - Classification model and training material for the detection of Rooftop Photovoltaic (RPV) systems.

DOI

This dataset contains:

A Convolution Neural Network trained to identify rooftop photovoltaic systems The training material, here a set of labelled images of rooftops, based in France, containing or not PV systems.

The training dataset is composed of our own data manually labelled as well as the data of Kasmi et al. https://10.1038/s41597-023-01951-4

This dataset is related to the scientific publication "Thebault, Nerot, Govehovitch, Ménézo - A comprehensive building-wise Residential Photovoltaic system detection in heterogeneous urban and rural areas: application to French territories" Applied Energy, 2025, doi.org/10.1016/j.apenergy.2025.125630

sgis, 0.1

tensorflow, 2.9.3

Identifier
DOI https://doi.org/10.57745/CH6QN4
Metadata Access https://entrepot.recherche.data.gouv.fr/oai?verb=GetRecord&metadataPrefix=oai_datacite&identifier=doi:10.57745/CH6QN4
Provenance
Creator NEROT, Boris; THEBAULT, Martin ORCID logo
Publisher Recherche Data Gouv
Contributor THEBAULT, Martin; NEROT, Boris; GOVEHOVITCH, Benjamin; Centre national de la recherche scientifique; Laboratoire procédés énergie bâtiment; Université Savoie Mont Blanc; Entrepôt-Catalogue Recherche Data Gouv
Publication Year 2024
Funding Reference Agence nationale de la recherche ANR-18-EURE-0016
Rights etalab 2.0; info:eu-repo/semantics/openAccess; https://spdx.org/licenses/etalab-2.0.html
OpenAccess true
Contact THEBAULT, Martin (CNRS); NEROT, Boris (Université Savoie Mont Blanc)
Representation
Resource Type Dataset
Format application/octet-stream; image/png; application/x-ipynb+json; text/markdown; application/gzip; text/x-python
Size 93976592; 57; 68191; 14346; 7806; 16921; 8042370700; 1162; 1074; 4571
Version 1.1
Discipline Computer Science; Engineering Sciences; Construction Engineering and Architecture; Engineering