A crowdsourced dataset of aerial images with annotated solar photovoltaic arrays and installation metadata

Photovoltaic (PV) energy generation plays a crucial role in the energy transition. Small-scale PV installations are deployed at an unprecedented pace, and their integration into the grid can be challenging since stakeholders often lack quality data about these installations. Overhead imagery is increasingly used to improve the knowledge of distributed PV installations with machine learning models capable of automatically mapping these installations. However, these models cannot be easily transferred from one region or data source to another due to differences in image acquisition. To address this issue known as domain shift and foster the development of PV array mapping pipelines, we propose a dataset containing aerial images, annotations, and segmentation masks. We provide installation metadata for more than 28,000 installations. We provide ground truth segmentation masks for 13,000 installations, including 7,000 with annotations for two different image providers. Finally, we provide ground truth annotations and associated installation metadata for more than 8,000 installations. Dataset applications include end-to-end PV registry construction, robust PV installations mapping, and analysis of crowdsourced datasets. This dataset contains the complete records associated with the article "A crowdsourced dataset of aerial images of solar panels, their segmentation masks, and characteristics", currently under review. The preprint is accessible here. These complete records consist of RGB overhead imagery, segmentation masks, and characteristics of PV installations. The data records are organized as follows: bdappv/ Root data folder google / ign: One folder for each campaign img/: Folder containing all the images presented to the users. This folder contains 28807 images for Google and 17325 images for IGN. mask/: Folder containing all segmentations masks generated from the polygon annotations of the users. This folder contains 13303 masks for Google and 7686 masks for IGN. metadata.csv The .csv file with the characteristics of the installations.光伏（Photovoltaic，PV）发电在能源转型中发挥着至关重要的作用。小型光伏装机正以前所未有的速度部署，但其并网集成却颇具挑战，因为相关利益方往往缺乏这类装机的高质量数据。高空遥感影像正愈发多地与可自动绘制这类装机分布的机器学习模型结合，用于提升对分布式光伏装机的认知水平。然而，由于影像采集方式存在差异，这类模型难以在不同区域或数据源之间实现迁移。为解决这一被称为领域漂移（domain shift）的问题，并推动光伏阵列测绘流程的发展，我们构建了一个包含航拍影像、标注数据与分割掩码的数据集。本数据集涵盖超过28000个光伏装机的元数据；为13000个光伏装机提供了真值标注（ground truth）分割掩码，其中7000个配有来自两家不同影像供应商的标注信息；此外还为超过8000个光伏装机提供了真值标注及对应的装机元数据。本数据集的应用场景涵盖端到端光伏台账构建、鲁棒性光伏装机测绘以及众包数据集分析。本数据集包含与当前处于审稿阶段的论文"A crowdsourced dataset of aerial images of solar panels, their segmentation masks, and characteristics"相关的完整数据记录，预印本可在此处获取。这些完整数据记录包括RGB高空影像、分割掩码以及光伏装机的各项特征参数。数据记录的组织形式如下： bdappv/：根数据文件夹 google / ign：对应各采集项目的子文件夹 img/：存放所有提供给用户的影像的文件夹。其中Google数据源包含28807张影像，IGN数据源包含17325张影像。 mask/：存放所有由用户多边形标注生成的分割掩码的文件夹。其中Google数据源包含13303个掩码，IGN数据源包含7686个掩码。 metadata.csv：包含光伏装机特征参数的CSV格式文件。