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

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, segmentation masks, and installation metadata (i.e., technical characteristics). We provide installation metadata for more than 28000 installations. We supply ground truth segmentation masks for 13000 installations, including 7000 with annotations for two different image providers. Finally, we provide installation metadata that matches the annotation for more than 8000 installations. Dataset applications include end-to-end PV registry construction, robust PV installations mapping, and analysis of crowdsourced datasets.

An Artificial Intelligence‐Based Framework to Accelerate Data‐Driven Policies to Promote Solar Photovoltaics in Lisbon

“…As recognized earlier, more locally labeled images would be nice to have for better estimates. But, as no further labeled data is expected to be produced in the near future, a method to refine the city‐wide PV power estimation will be explored based on the Downstream Task Accuracy proposed in the study of Kasmi et al [ 21 ]…”

“…Combined with highresolution aerial images, 3D allowed for a detailed mapping and characterization of PV across the city, also used to monitor the increasing number of PV installations in recent years. Kasmi et al and Stowell et al [9,10] have also recurred to aerial and satellite images of cities to similar ends but focused on open data and crowdsourcing for the training sets of the AI models. In the study of Kausika et al, [11] the potential for improved policy-making and infrastructure planning based on such geolocation is highlighted.…”

“…Hence, the dataset from the study of Hou et al [16] was also incorporated: three groups of samples were available, each collected at different spatial resolutions of 0.8, 0.3, and 0.1 m. The model fine-tuned on the second dataset (i.e., our model "v3") had better accuracy, but it was still struggling with several false positives. Finally, one last dataset with features closer to the ones of Lisbon was retrieved from the study of Kasmi et al, [9] consisting of an open-source dataset of more than 15 000 images put together via crowdsourcing.…”

“…As recognized earlier, more locally labeled images would be nice to have for better estimates. But, as no further labeled data is expected to be produced in the near future, a method to refine the city-wide PV power estimation will be explored based on the Downstream Task Accuracy proposed in the study of Kasmi et al [21] Considering the ranking of the parishes, we can verify that Olivais, and Lumiar were by far the most solar ones in 2016, which is not surprising since these feature some of the sociodemographic aspects that motivated an early adoption of rooftop PV: predominance of single-family buildings in low-rise neighborhoods, higher income households, and higher education levels. These are also some of the most densely built parishes and are not subject to rules of landscape such as in the city center and historical areas.…”

Data Augmentation with Generative Adversarial Network for Solar Panel Segmentation from Remote Sensing Images

“…The data used for semantic segmentation and GAN model training is a collection of five solar panel aerial image data sets. The Provincial Geomatics Center of Jiangsu provides three data sets [24], and two are sourced from Google Earth and the French National Institute of Geographical and Forestry Information (IGN) [25]. The data sets are presented and compared in Table 1.…”