Photovoltaic panel dataset

What is a photovoltaic (PV) dataset?

A photovoltaic (PV) dataset from satellite and aerial imagery. The dataset includes three groups of PV samples collected at the spatial resolution of 0.8m, 0.3m and 0.1m, namely PV08 from Gaofen-2 and Beijing-2 imagery, PV03 from aerial photography, and PV01 from UAV orthophotos. PV08 contains rooftop and ground PV samples.

How many pairs of PV panels are in the PvP dataset?

The PVP Dataset contains 4640 pairs image of PV panel samples from 13 provinces in China. The samples in PVP Dataset were collected by Google Earth, Tianditu and Mapbox.Each group of samples in composed a image of 512×512 pixels and a corresponding label of PV panels.

Which dataset is used for PV panel segmentation?

The utilized dataset is from the multi-resolution dataset for PV panel segmentation published by Jiang et al. . This dataset contains 3716 samples annotated in Jiangsu Province, China, including different types of PVs such as centralized PVs, distributed ground-mounted PVs, and fine-grained rooftop PVs. ... ...

How many solar PV installations are there in the UK?

We present the results of a major crowd-sourcing campaign to create open geographic data for over 260,000 solar PV installations across the UK, covering an estimated 86% of the capacity in the country.

Which datasets include annotated solar panels in native resolution and HD satellite imagery?

The complete dataset contains native resolution satellite imagery, corresponding HD imagery, and solar panel object labels for each image type (Fig. 1). To the best knowledge of the authors, there are no publicly available datasets including annotated solar panels in native resolution and HD satellite imagery.

Is pvnet a semantic segmentation model for photovoltaic panels?

Photovoltaic Panel (PVP) Dataset was publicly available in paper "PVNet: A novel semantic segmentation model for extracting high-quality photovoltaic panels in large-scale systems from high-resolution remote sensing imagery" on International Journal of Applied Earth Observation and Geoinformation.