DeepSolar for Germany: A deep learning framework for PV system mapping from aerial imagery
Kevin Mayer, Zhecheng Wang, Marie-Louise Arlt, Dirk Neumann, Ram Rajagopal
Abstract
The increasing availability of high-resolution aerial imagery and the recent deep learning-based advances in computer vision have made it possible to automatically map energy systems remotely at a large scale. In this paper, we focus on optimizing the existing DeepSolar framework for photovoltaics (PV) system classification. Specifically, we propose an efficient dataset creation methodology for aerial imagery which allows us to achieve state-of-the-art results, improving the previous model's recall score by more than eight percentage points to 98% while keeping its precision almost constant at 92%. Furthermore, we show that our optimized model extends its superior classification performance to lower image resolutions. After re-training our optimized model on lower resolution imagery, we apply it to Germany's most-populous state, North-Rhine Westphalia, and deliver a proof of concept for automatically validating, updating, and creating databases of renewable energy systems at a large scale. We conclude with a brief analysis of socio-economic factors correlating with PV system adoption.