ABSTRACT Photovoltaic (PV) energy generation plays a crucial role in the energy transition. Small-scale, rooftop PV installations are deployed at an unprecedented pace, and their safe

integration into the grid requires up-to-date, high-quality information. Overhead imagery is increasingly being used to improve the knowledge of rooftop PV installations with machine

learning models capable of automatically mapping these installations. However, these models cannot be reliably transferred from one region or imagery source to another without incurring a

decrease in accuracy. To address this issue, known as distribution shift, and foster the development of PV array mapping pipelines, we propose a dataset containing aerial images,

installations. Dataset applications include end-to-end PV registry construction, robust PV installations mapping, and analysis of crowdsourced datasets. SIMILAR CONTENT BEING VIEWED BY

OTHERS GEORECTIFIED POLYGON DATABASE OF GROUND-MOUNTED LARGE-SCALE SOLAR PHOTOVOLTAIC SITES IN THE UNITED STATES Article Open access 08 November 2023 A HARMONISED, HIGH-COVERAGE, OPEN

DATASET OF SOLAR PHOTOVOLTAIC INSTALLATIONS IN THE UK Article Open access 13 November 2020 A GLOBAL INVENTORY OF PHOTOVOLTAIC SOLAR ENERGY GENERATING UNITS Article 27 October 2021 BACKGROUND

& SUMMARY In 2021, photovoltaic (PV) power generation amounted to 821 _TWh_ worldwide and 14.3 _TWh_ in France1. With an installed capacity of about 633 _GW__p_ worldwide2 and 13.66

_GW__p_ in France, PV energy represents a growing share of the energy supply. The integration of growing amounts of solar energy in energy systems requires an accurate estimation of the

produced power to maintain a constant balance between electricity demand and supply. However, small-scale PV installations are generally invisible to transmission system operators (TSOs),

meaning their generated power is not monitored3. For TSOs, the lack of reliable rooftop PV measurements increases the flexibility needs, i.e., the means for the grid to compensate for load

or supply variability4,5,6,7. Estimating the PV power generation from meteorological data is common practice to overcome the lack of power measurements. However, this requires precise

information on PV installed capacity and installation metadata8,9. Detailed information regarding small-scale PV is therefore of interest for integrating renewable energies into the grid10,

or for understanding the factors behind its development11. Currently, such PV installation registries covering large areas are neither easily available nor available everywhere. Recent

research to construct global PV inventories12,13 is limited to solar farms and does not include rooftop PV. A recent crowdsourcing effort allowed the mapping of 86% of the United Kingdom’s

rooftop PV installations14. Other available datasets are aggregated at the communal scale (census level)15. Remote sensing-based methods13,15,16,17 recently emerged as a promising solution

to quickly and cheaply acquire detailed information on PV installations18. These methods rely on overhead imagery and deep neural networks. The DeepSolar initiative led to the mapping of

rooftop PV installations over the continental United-States15 or the state of North-Rhine Westphalia19. These remote sensing-based methods cannot scale to unseen regions without a sharp

decrease in accuracy20,21. It is caused by the sensitivity of deep learning models to distribution shifts22 (i.e., when _“the training distribution differs from the test distribution_”23).

These distribution shifts typically correspond to acquisition conditions and architectural differences across regions24 (e.g., building shape, vegetation). The lack of robustness to

distribution shifts limits the reliability of deep learning-based registries for constructing official PV statistics10. Therefore, developing PV mapping algorithms that are robust to

distribution shifts is necessary. To encourage the development of such algorithms, we introduce a training dataset containing data for (i) addressing distribution shifts in remote sensing

applications and (ii) helping design algorithms capable of extracting small-scale PV metadata from overhead imagery. To address distribution shifts, we gathered ground-truth annotations from

two image providers for installations located in France. The double annotation allows researchers to evaluate the robustness of their approach to a shift in data provider (which affects

acquisition conditions, acquisition device, and ground sampling distance) while keeping the same observed object. Our dataset provides ground truth installation masks for 13303 images from

Google Earth25 and 7686 images from the French national institute of geographical and forestry information (IGN). To address architectural differences, researchers can either use the

coarse-grained location included in our dataset or use our dataset in conjunction with other training datasets that mapped different areas (e.g., Bradbury _et al_.26 or Khomiakov _et

al_.27). To extract PV systems’ metadata, we release the installation metadata for 28807 installations. This metadata includes installed capacity, surface, tilt, and azimuth angles,

sufficient for regional PV power estimation8. We linked the installation metadata and the ground truth images for 8019 installations. To the best of our knowledge, it is the first time a

training dataset contains PV panel images, ground truth labels, and installation metadata. We hope this data contributes to the ongoing effort to construct more detailed PV registries. We

obtained our labels through two crowdsourcing campaigns conducted in 2021 and 2022. Crowdsourcing is common practice in the machine learning community for annotating training datasets28,29.

We developed our crowdsourcing platform and collected up to 50 annotations per image to maximize the accuracy of our annotations. Besides, multiple annotations per image facilitate

measurement of the annotator’s agreement or limit their individual annotations biases. Indeed, we found that some annotators were more cautious when annotating than others. We make the raw

crowdsourcing data publicly available. It allows the replication of our annotations, but we also hope this will help research crowdsourcing, e.g., on the efficient combination of labels30.

Our dataset targets practitioners and researchers in machine learning and crowdsourcing. Our data can serve as training data for remote PV mapping algorithms and test machine learning

models’ robustness against acquisition conditions or geographical shift. Additionally, we release the raw annotation data from the crowdsourcing campaigns for the community to carry out

further studies on the fusion of multiple annotations into ground truth labels. The training dataset and the data coming from the crowdsourcing campaigns are accessible on our Zenodo

repository31. METHODS We illustrate our training dataset generation workflow in Fig. 1. It comprises three main steps: raw data extraction, thumbnails annotation, and metadata matching. RAW

DATA EXTRACTION Our annotation campaign leverages the database of PV systems operated by the non-profit association _Asso BDPV_ (_Base de données Photovoltaïque_ - Photovoltaic database).

_Asso BDPV_ (BDPV) gathers metadata (geolocation and metadata of the PV systems) and the energy production data of PV installations provided by individual system owners, mainly in France and

Western Europe. The primary purpose of the BDPV database is to monitor system owners’ energy production. BDPV also promotes PV energy by disseminating information and data to the general

public and public authorities. The BDPV data contains the localization of more than 28000 installations. We used this localization to extract the panels’ thumbnails. During the first

annotation campaign, we extracted 28807 thumbnails using Google Earth Engine (GEE)25 application programming interface (API). For the second campaign, we extracted 17325 thumbnails from the

IGN Geoservices portal (https://geoservices.ign.fr/bdortho). Our thumbnails all have a resolution of 400 × 400 pixels. Thumbnails extracted from GEE API correspond to a ground sampling

distance (GSD) of 0.1 m/pixel. The API directly generates this thumbnail by setting the zoom level to 20, the localization to the ground truth localization contained in BDPV, and the output

size to be 400 × 400 pixels. For IGN images, the resolution of the thumbnails corresponds to a GSD of 0.2 m/pixel. The procedure for generating IGN thumbnails differs from Google. First, we

downloaded geo-localized tiles from IGN’s Geoservices portal. These tiles have a resolution of 25000 × 25000 pixels, covering an area of 25 square kilometers. Then, we extracted the

thumbnail by generating a 400 × 400 pixels raster centered around the location of the PV panel. Finally, we export this raster as a.png file. We do not publish the exact location of the

panels for confidentiality reasons. The crowdsourcing campaigns took place on a dedicated platform called BDAPPV, which stands for _“Base de données apprentissage profond PV”_ (PV database

for deep learning). The BDAPPV platform is a web page where users can ergonomically annotate aerial images by clicking on the panel (phase 1) or delineating polygons around the PV panels

(phase 2). Table 1 summarizes the contribution of the annotators during the crowdsourcing campaigns. The web page is accessible at this URL: https://www.bdpv.fr/_BDapPV/. THUMBNAILS

ANNOTATION We extracted thumbnails based on the geolocation of the installations recorded in the BDPV dataset. However, this geolocation can be inaccurate, so before asking users to draw

polygons of PV installations, we asked them to classify the images. It corresponds to the first phase of the annotation campaign. Once users classified images, we asked them to draw the PV

polygons on the remaining images. It corresponds to the second phase of the crowdsourcing campaign. We designed our campaign to get at least five annotations per image. It enabled us to

derive so-called _consensus metrics_, targeted at measuring the quality of our labels. This way, we go further than the consensus between two annotators reported in previous work26 to

measure annotation quality. The analysis of the users’ annotations during phases 1 and 2 are reproducible using the notebook annotations available on the public repository, accessible at

this URL: https://github.com/gabrielkasmi/bdappv/. PHASE 1: IMAGE CLASSIFICATION During the first phase, the user clicks on an image if it depicts a PV panel. We recorded the localization of

the user’s click and instructed them to click _on_ the PV panel if there was one. We collected an average of 10 actions (click with localization or no click) per image. The left panel of

Fig. 2 provides an example of annotations during phase 1. We apply the kernel density estimate (KDE) algorithm to the annotations to estimate a confidence level for the annotations and the

approximate localization of the PV panel on the image. The likelihood _f__σ_ (_x__i_) of presence of a panel for each pixel _x__i_ is given by: $${f}_{\sigma

}({x}_{i})=\frac{1}{N}\mathop{\sum }\limits_{k=1}^{N}{K}_{\sigma }\left({x}_{k}-{x}_{i}\right)$$ (1) where _K__σ_ is a Gaussian kernel with a standard deviation _σ_, _x__k_ is the coordinate

of the _k__th_ annotation, and _N_ is the total number of annotations. After an empirical investigation, we calibrated the standard deviation of the kernel to reflect the approximate

spatial extent of an array on the image. We set its value to 25 pixels for Google images and 12 for IGN images. It corresponds to a distance of 2.5 m. As illustrated on Fig. 2, the KDE

yields a heatmap whose hotspot locates on the solar array. The maximum value of the KDE quantifies the confidence level of the annotation. We refer to it as the _pixel annotation consensus_

(PAC). This metric is proportional to the number of annotations. We use the PAC to determine whether an image contains an array. PHASE 2: POLYGON ANNOTATION During the second phase,

annotators delineate the PV panels on the images validated during phase 1. Users can draw as many polygons as they want. On average, we collected five polygons per image. We collect the

coordinates of the polygons drawn by the annotators. As illustrated in the lower left panel of Fig. 2, a set of polygons is available for each array in an image. We can note from the

annotation illustrated in Fig. 2 that some polygons may be erroneous. However, these false positives have fewer annotations than true positives. To select only the true positives, we compute

the PAC through the following steps: * 1. We convert each user’s polygon into a binary raster; * 2. We compute the normalized PAC by summing all rasters and dividing by the number of

annotators, * 3. We apply a relative threshold and keep only the pixels whose PAC is greater than the threshold; * 4. We compute the coordinates of the resulting mask using OpenCV’s polygon

detection algorithm (https://docs.opencv.org/3.4/d4/d73/tutorial_py_contours_begin.html). In step 2., the unnormalized PAC takes values between 0 and the number _N__i_ of annotators for the

_i__th_ image. 0 means no user included the pixel into his polygon, and _N__i_ means that _all_ annotators encapsulated the corresponding pixel in their polygons. METADATA MATCHING Once we

generate our PV panels polygons (i.e. segmentation masks), we match them with the installations’ metadata reported in the BDPV dataset. Our matching procedure follows three steps: internal

consistency, unique matching, and external consistency. Note that we only apply these filters when matching the metadata and the masks. INTERNAL CONSISTENCY ensures that the entries in the

BDPV dataset are coherent before any matching. It is simply a cleaning of the raw dataset. To do this cleaning, we verify whether the information in one column of the BDPV dataset is

coherent with the records from the other columns. For instance, if a PV system’s record says it has ten modules and a surface of 3 squared meters, this would mean that each PV module has a

surface of 0.3 squared meters, which is impossible (the smallest size being 1.7 squared meters). UNIQUE MATCHING Our segmentation masks may depict more than one array. It occurs if, for

instance, more than one panel is on the image shown to the annotators. In this case, we adopt a conservative view: if the segmentation mask depicts more than one panel, we cannot know which

one corresponds to the installation reported in the BDPV dataset. We do not match the segmentation mask with an installation in this case. EXTERNAL CONSISTENCY After internal consistency

filtering and unique matching, we are left with segmentation masks depicting single panels with coherent metadata. A final filtering step consists in making sure that the characteristics

reported in the database match those that can be deduced from the segmentation mask. We assess the adequacy between the surface of the installation’s mask and its true surface reported in

the BDPV dataset by computing the ratio between them. We keep only installations whose ratio is equal to 1 (with a tolerance bandwidth of ±25%). We apply this bandwidth to accommodate the

possible approximations in the segmentation mask. The reported surface excludes the inter-panel space and the distortions induced by the panel’s projection on the image, as images are not

perfectly orthorectified. DATA RECORDS The data records consist of two separate datasets, accessible on our Zenodo repository31, at this URL: https://zenodo.org/record/7358126. * 1. The

_training dataset_: input images, segmentation masks, and PV installations’ metadata, * 2. The _crowdsourcing and replication data_: annotations from the users, for each image, provided

in.json format and the raw installations’ metadata. Besides, the source code and notebooks used to generate the masks from the users’ annotations are accessible on our public Git repository

at this URL: https://github.com/gabrielkasmi/bdappv. This repository contains the source code used to generate the segmentation masks. It contains the notebooks annotations and metadata,

which can be used to visualize the threshold analysis or the metadata matching procedure. TRAINING DATASET The training dataset containing RGB images, ready-to-use segmentation masks of the

two campaigns, and the file containing PV installations’ metadata is accessible on our Zenodo repository. It is organized as follows: * bdappv/ Root data folder * google/ign One folder for

each campaign * img Folder containing all the images presented to the annotators. This folder contains 28807 images for Google and 17325 for IGN. We provide all images as.png files. * mask

Folder containing all segmentation masks generated from the polygon annotations of the annotators. This folder contains 13303 masks for Google and 7686 for IGN. We provide all masks as.png

files. * - metadata.csv The.csv file with the metadata of the installations. Table 7 describes the attributes of this table. CROWDSOURCING AND REPLICATION DATA The Git repository contains

the raw crowdsourcing data and all the material necessary to re-generate our training dataset and technical validation. It is structured as follows: the raw subfolder contains the raw

annotation data from the two annotation campaigns and the raw PV installations’ metadata. The replication subfolder contains the compiled data used to generate our segmentation masks. The

validation subfolder contains the compiled data necessary to replicate the analyses presented in the technical validation section. * data/ Root data folder * raw/ Folder containing the raw

crowdsourcing data and raw metadata; * input-google.json: Input data containing all information on images and raw annotators’ contributions for both phases (clicks and polygons) during the

first annotation campaign; * input-ign.json: Input data containing all information on images and raw annotators’ contributions for both phases (clicks and polygons) during the second

annotation campaign; * raw-metadata.csv: The file containing the PV systems’ metadata extracted from the BDPV database before filtering. It can be used to replicate the association between

the installations and the segmentation masks, as done in the notebook metadata. Table 6 describes the attributes of the raw-metadata.csv table. * - replication/ Folder containing the

compiled data used to generate the segmentation masks; * campaign-google/campaign-ign. One folder for each campaign * click-analysis.json: Output on the click analysis, compiling raw input

into a few best-guess locations for the PV arrays. This dataset enables the replication of our annotations; * polygon-analysis.json: Output of polygon analysis, compiling raw input into a

best-guess polygon for the PV arrays. * validation/ Folder containing the compiled data used for technical validation. * campaign-google/campaign-ign. One folder for each campaign *

click-analysis-thres = 1.0.json: Output of the click analysis with a lowered threshold to analyze the effect of the threshold on image classification, as done in the notebook annotations; *

polygon-analysis-thres = 1.0.json: Output of polygon analysis, with a lowered threshold to analyze the effect of the threshold on polygon annotation, as done in the notebook annotations. *

metadata.csv the filtered installations’ metadata. RAW CROWDSOURCING DATA AND RAW INSTALLATIONS’ METADATA The files input-google.json and input-ign.json provide the raw crowdsourcing data

for both annotation campaigns. The two files are identically formatted. They contain all metadata for all images and the associated annotators’ contributions for both phases (clicks and

polygons). It enumerates all the clicks and polygons attached to the image. Coordinates are expressed in pixels relative to the upper-left corner of the image. We also provide information on

the click or the polygon (annotator’s ID, date, country). The field “Clicks list” is empty when an image contains no clicks. The field “Polygons list” is empty if it does not contain

polygons. Table 3 describes the attributes of the input tables. The raw metadata datasheet corresponds to the extraction of the BDPV database. This file contains all the installations’

metadata. Table 6 provides a list of the complete attributes. We use this file as input to associate the segmentation masks to the installations’ metadata. REPLICATION OF THE IMAGE

CLASSIFICATION AND POLYGON ANNOTATION We compiled the files click-analysis.json and polygon-analysis.json from the raw inputs to classify the images and generate the segmentation masks,

respectively. We provide these files to enable users to replicate our classification process and the generation of our masks. The output of click analysis contains a list of detected PV

installations’ positions for each image. Each image contains at least one point, corresponding to the number of panels found in it. The score variable summarizes the PAC associated with each

point. By construction, the click-analysis.json files only contain points with a PAC greater than 2.0 (see the technical validation section for more details on the threshold tuning). Table 

4 describes the attributes of the click-analysis.json data file. The output of polygon analysis contains a list of polygons as a compilation of all polygons annotated by annotators. It

contains one or more polygons for each image corresponding to the PV arrays. Analogous to the click-analysis files, this file summarizes the polygon annotations of the users. The variable

score records the relative PAC associated with each polygon. By construction, the polygon-analysis.json files only contain polygons with a relative PAC greater than 0.45 (see the technical

validation section for more details on the threshold tuning). Table 5 describes the attributes of the polygon-analysis.json data files. VALIDATION DATA We compiled the files

click-analysis-thres = 1.0.json and polygon-analysis-thres = 1.0.json to enable users to study how the thresholds chosen to generate our annotations affect the segmentation masks that we

generate. The notebook annotations enables us to carry out this study. The metadata.csv file corresponds to the output file of the notebook metadata. We provide this file to enable users to

replicate our analysis of the fit between the filtered installations and their segmentation masks. TECHNICAL VALIDATION Throughout the generation of the training dataset, we tested whether

the threshold values chosen to classify the images, construct the polygon and associate the polygons to the installations’ metadata yielded as few errors as possible. We base our approach on

a consensus metric to classify images and construct the polygons, namely the pixel annotation consensus (PAC). Thus, we improve on Bradbury _et al_.26, who proposed a confidence value based

on the Jaccard Similarity Index32 between the two annotations. As for the association between the polygons and installations’ metadata, we balance between accuracy and keeping as many

installations as possible. ANALYSIS OF THE CONSENSUS VALUE FOR IMAGE CLASSIFICATION As mentioned in the methods section, the choice criterion for image classification during phase 1 is the

consensus among users. We empirically investigated a range of thresholds and determined that a value of 2.0 yielded the most accurate classification results. In other words, we require that

at least three annotators click around the same point to validate the classification. We use an _absolute_ (unnormalized by the number of annotators for this image) threshold to decide

whether the image contains a panel. The threshold is absolute because users could only click once on the image during the annotation campaign, even if the latter contained more than one

array. As such, an absolute threshold does not dilute the consensus among users when there is more than one panel on the image. The leftmost plot of Fig. 3 plots the histogram of the

absolute PAC. Visual inspection revealed that the peak for values below 2.0 corresponded to false positives. We enable replication of the threshold analysis in the notebook annotation.

ANALYSIS OF THE CONSENSUS VALUE OF THE POLYGON ANNOTATION Like the click annotation, we used a consensus metric to merge the users’ annotations. After empirical investigations, we found that

a _relative_ threshold (expressed as a share of the total number of annotators) was the most effective for yielding the most accurate masks and that its value should be 0.45. In other

words, we consider that a pixel depicts an installation if at least 45% of the annotators included it in their polygons. The center plot of Fig. 3 depicts the histogram of the relative PAC.

Visual inspection revealed that the few values below 0.45 corresponded to remaining false positives (e.g., roof windows). The use of a relative threshold is motivated by the fact that the

users can annotate as many polygons as they want. We enable replication of the threshold analysis in the notebook annotation. CONSISTENCY BETWEEN POLYGON ANNOTATIONS AND METADATA OF THE PV

INSTALLATIONS We link segmentation masks and installations metadata according to the steps described in the section “Metadata matching.” To measure the quality of this linkage, we measure

the Pearson correlation coefficient (PCC) between the surface reported in the installation’ metadata dataset (referred to as the “target” surface) and the surface estimated from the

segmentation masks (referred to as the “estimated” surface). The higher the PCC, the better our matching procedure. Figure 3 plots estimated and target surfaces. After filtering, we obtain a

PCC coefficient of 0.99 between the target and estimated surfaces. Without filtering, the PCC coefficient equals 0.68 for Google images and 0.61 for IGN images. It shows that our

metadata-matching procedure enabled us to pick the installations with the best fit between the reported surface and the surface estimated from the segmentation masks. Our matching procedure

comprises three steps: internal consistency, uniqueness and external consistency. Each of these steps discards installations from the BDPV database. Table 2 summarizes the number of

installations filtered at each process step. We can see that most of the filtering happens when we discard segmentation masks on which there is more than one installation. USAGE NOTES We

designed the complete dataset records to be directly used as training data in machine learning projects. The ready-to-use data is accessible on our Zenodo repository accessible at this URL

https://zenodo.org/record/7358126. This repository also stores the raw crowdsourcing data and the files necessary to reproduce our segmentation masks and analyses. We compiled the files

click-analysis.json and polygon-analysis.json using the Python scripts click-analysis.py and polygon-analysis.py, provided in our repository from the raw input data input-{provider}.json.

This repository also contains the notebooks annotations and metadata. The notebook annotations presents the analysis of crowdsourced data from the crowdsourcing campaigns. The notebook

metadata filters the raw-metadata.csv datasheet. Between phases 1 and 2, we generated new thumbnails re-centered on the PV installations. The new center corresponds to the coordinates of the

estimated center of the (first in the list) detected PV installation. Therefore, to replicate the click analysis on the corresponding image, interested users need to download the

corresponding image accessible on the BDAPPV website as illustrated in the notebook annotations. We re-center images by generating a new thumbnail centered around an updated location,

according to the procedure described in the section “methods”. The centering of the images will not induce a bias during learning because our thumbnails have a larger resolution (400 × 400

pixels) than the typical input size of typical neural networks (224 × 224 pixels). Adding a random crop transform during training will result in panels not being centered anymore. Besides,

during the IGN campaign, we only re-centered about 13% of the images. RIGHTS AND PERMISSIONS Code, raw crowdsourcing data, and compiled data are accessible on the project repository. All

materials are provided under the CC-BY license. This license allows reusers to distribute, remix, adapt, and build upon the material in any medium or format, as long as attribution is given

to the creator. The license allows for commercial use. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation,

distribution, and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and

indicate if changes were made. The images or third-party material in this article are included in the article’s Creative Commons license unless indicated otherwise in a credit line to the

material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use. In that case, you

will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain

Dedication waiver http://creativecommons.org/publicdomain/zero/1.0/ applies to the metadata files associated with this article. CODE AVAILABILITY Our public repository accessible at this URL

https://github.com/gabrielkasmi/bdappv contains the code to generate the masks, filter the metadata and analyze our results. Interested users can clone this repository to replicate our

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ACKNOWLEDGEMENTS We want to thank all annotators who participated in the crowdsourcing campaigns. We also would like to thank the association BDPV and the users of the online forum _Forum

Photovoltaïque_ (https://forum-photovoltaique.fr/). They actively participated in the execution of the crowdsourcing elaboration of this project. This project is carried out as part of the

Ph.D. thesis of Gabriel Kasmi, sponsored by the French transmission system operator RTE France and partly funded by the national agency for research and technology (ANRT) under the CIFRE

contract 2020/0685. The European Commission partially funds the work of Jonathan Leloux and Babacar Sarr through the Horizon 2020 project SERENDI-PV (https://serendipv.eu/), which belongs to

the Research and Innovation Programme, under Grant Agreement 953016. AUTHOR INFORMATION Author notes * These authors contributed equally: Gabriel Kasmi, Yves-Marie Saint-Drenan, David

Trebosc, Raphaël Jolivet. AUTHORS AND AFFILIATIONS * Mines Paris - PSL University, Centre Observation, Impacts, Energy (O.I.E.), 06904, Sophia Antipolis, France Gabriel Kasmi, Yves-Marie

Saint-Drenan & Raphaël Jolivet * RTE France, 7C place du Dôme, 92073, Paris La Défense, France Gabriel Kasmi & Laurent Dubus * BDPV, 1 Rue du Capitaine Fracasse, 31320, Castanet

Tolosan, France David Trebosc * LuciSun, Rue Saint-Jean, 29, Sart-Dames-Avelines, Villers-la-Ville, Belgium Jonathan Leloux & Babacar Sarr Authors * Gabriel Kasmi View author

publications You can also search for this author inPubMed Google Scholar * Yves-Marie Saint-Drenan View author publications You can also search for this author inPubMed Google Scholar *

David Trebosc View author publications You can also search for this author inPubMed Google Scholar * Raphaël Jolivet View author publications You can also search for this author inPubMed 

Google Scholar * Jonathan Leloux View author publications You can also search for this author inPubMed Google Scholar * Babacar Sarr View author publications You can also search for this

author inPubMed Google Scholar * Laurent Dubus View author publications You can also search for this author inPubMed Google Scholar CONTRIBUTIONS G.K.: Contribution to the concept

development, contributions to the manuscript review and editing of the final draft, second campaign management (generation of the thumbnails from IGN data for the two phases), matching

between the metadata and the installations, Figs. 1,3 tables formatting, crowdsourcing campaign analysis, manuscript revision. R.J.: Contribution to manuscript and code for annotations

analysis (scripts and notebooks), generation of the ground truth labels, Fig. 2 and tables formatting. Y.-M. S.-D.: development of the concept of BDAPPV, contribution and reviews to the

original manuscript, analysis of the annotation, Figs. 2,3 formatting, manuscript revision. D.T.: development of the concept of BDAPPV, conception and development of the annotation platform,

communication with the community of annotators, collection and management of the annotation data, and contribution to the manuscript. J.L.: contribution to the concept development and

review of the manuscript. B.S.: contribution to the concept development and review of the manuscript. L.D.:contribution to the concept development and review of the manuscript. CORRESPONDING

AUTHOR Correspondence to Gabriel Kasmi. ETHICS DECLARATIONS COMPETING INTERESTS Y.-M. Saint-Drenan and D. Trebosc are members of the non-profit association _Asso BDPV_, which collects and

maintains a database of rooftop PV plants. Y.-M. Saint-Drenan, D. Trebosc, J. Leloux, and B. Sarr are members of the initiative coBDPV, aiming to improve the analysis conducted within the

BDPV platform. G. Kasmi is carrying out a Ph.D. funded by RTE (the French transmission system operator) and the national agency for research and technology (ANRT). L. Dubus is a senior

research scientist at RTE. R. Jolivet declares no conflict of interest. ADDITIONAL INFORMATION PUBLISHER’S NOTE Springer Nature remains neutral with regard to jurisdictional claims in

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permissions ABOUT THIS ARTICLE CITE THIS ARTICLE Kasmi, G., Saint-Drenan, YM., Trebosc, D. _et al._ A crowdsourced dataset of aerial images with annotated solar photovoltaic arrays and

installation metadata. _Sci Data_ 10, 59 (2023). https://doi.org/10.1038/s41597-023-01951-4 Download citation * Received: 08 September 2022 * Accepted: 10 January 2023 * Published: 28

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