# Application of a semantic segmentation convolutional neural network for accurate automatic detection and mapping of solar photovoltaic arrays in aerial imagery

arXiv: https://arxiv.org/abs/1801.04018 · HTML: https://mlsanity.com/paper/1801.04018/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jan 2018.

## Built on by (most cited first, 4 of 4)

- [HyperionSolarNet: Solar Panel Detection from Aerial Images](https://mlsanity.com/paper/2201.02107/) (2022)
- [Automated Extraction of Energy Systems Information from Remotely Sensed Data: A Review and Analysis](https://mlsanity.com/paper/2202.12939/) (2022)
- [Solar Panel Mapping via Oriented Object Detection](https://mlsanity.com/paper/2502.03592/) (2025)
- [Evaluating Semantic and Spatial Guidance for Foundation Model Segmentation of Small-Scale PV in Remote Sensing Imagery](https://mlsanity.com/paper/2608.10801/) (2026)

## Abstract (arXiv)

We consider the problem of automatically detecting small-scale solar photovoltaic arrays for behind-the-meter energy resource assessment in high resolution aerial imagery. Such algorithms offer a faster and more cost-effective solution to collecting information on distributed solar photovoltaic (PV) arrays, such as their location, capacity, and generated energy. The surface area of PV arrays, a characteristic which can be estimated from aerial imagery, provides an important proxy for array capacity and energy generation. In this work, we employ a state-of-the-art convolutional neural network architecture, called SegNet (Badrinarayanan et. al., 2015), to semantically segment (or map) PV arrays in aerial imagery. This builds on previous work focused on identifying the locations of PV arrays, as opposed to their specific shapes and sizes. We measure the ability of our SegNet implementation to estimate the surface area of PV arrays on a large, publicly available, dataset that has been employed in several previous studies. The results indicate that the SegNet model yields substantial performance improvements with respect to estimating shape and size as compared to a recently proposed convolutional neural network PV detection algorithm.