Can satellite images capture photovoltaic panels

Can satellite imagery be used to identify solar PV systems?

One possible solution to this problem is to identify existing solar PV generation systems using overhead satellite and aerial imagery. While there have been early promising attempts in this direction, there are nevertheless many important research challenges that remain to be addressed.

Can satellite and aerial imagery identify rooftop solar PV systems?

The exact locations and capacity of rooftop solar PV systems are often not logged, and central registers of distributed solar PV can be incomplete or out of date. As a result, there is a growing interest in using satellite and aerial imagery to automatically identify solar PV systems’ locations and capacities across large regions.

Can satellite images be used for PV power forecast?

Nowadays, the common temporal resolution of publicly accessible satellite images is one hour , , which cannot match the general temporal resolution requirements of the ultra-short-term PV power forecast, e.g., 15 min for most power grids worldwide. This obstacle also limits the application of satellite images on PV power forecast.

Can satellite and aerial photography provide accurate PV information?

With the advance of spatiotemporal resolution of onboard sensors, satellite and aerial photography can provide up-to-date images of specific ground targets, making them an ideal source for obtaining accurate PV information (Perez et al., 2001; Peters et al., 2018; Wang et al., 2018).

Are annotated solar panels available in native resolution and HD satellite imagery?

To the best knowledge of the authors, there are no publicly available datasets including annotated solar panels in native resolution and HD satellite imagery. The process for creating the paired native resolution and HD image tiles and associated labels. Both sets of components contain three image tiles and 2,542 annotated solar panels.

Can satellite imagery improve solar panel detection accuracy?

We address these limitations by providing a solar panel dataset derived from 31 cm resolution satellite imagery to support rapid and accurate detection at regional and international scales. We also include complementary satellite imagery at 15.5 cm resolution with the aim of further improving solar panel detection accuracy.