SolarFinder: Automatic Detection of Solar Photovoltaic Arrays

Smart cities, utilities, third-parties, and government agencies are having pressure on managing stochastic power generation from distributed rooftop solar photovoltaic (PV) arrays, such as predicting and reacting to the variations in electric grid. Recently, there is a rising interest to identify solar PV arrays automatically and passively. Traditional approaches such as online assessment and utilities interconnection filings are time consuming and costly, and limited in geospatial resolution, and thus do not scale up to every location. Significant recent work focuses on using aerial imagery to train machine learning or deep learning models to automatically detect solar PV arrays. Unfortunately, these approaches typically require Very High Resolution (VHR) images and human handcrafted solar PV array templates for training, which have a minimum cost of $15 per km(2) and are not always available at every location. To address the problem, we design a new system-SolarFinder that can automatically detect: distributed solar PV arrays in a given geospatial region without any extra cost. SolarFinder first automatically fetches regular resolution satellite images within the region using publicly-available imagery APIs. Then, SolarFinder leverages multi-dimensional K-means algorithm to automatically segment solar arrays on rooftop images. Eventually, SolarFinder employs hybrid linear regression approach that integrates support vector machine (SVM) modeling with a deep convolutional neural networks (CNNs) approach to accurately identify solar PV arrays and characterize each solar deployment. We evaluate SolarFinder using 269,632 satellite images that include 1,143,636 contours from 13 geospatial regions in U.S. We find that pre-trained-SolarFinder yields a Matthews Correlation Coefficient (MCC) of 0.17, which is 3 times better than the most recent: pre-trained CNNs approach and the same as a re-trained CNNs approach.