Enhanced Rooftop Solar Panel Detection by Efficiently Aggregating Local Features

TL;DR

This paper introduces an improved CNN-based method for rooftop solar panel detection in satellite images, combining local feature aggregation with a transfer learning approach to adapt across different cities efficiently.

Contribution

It proposes a novel feature aggregation technique using VLAD and a 3-phase transfer learning approach for multi-city rooftop PV detection.

Findings

Achieved over 0.9 classification accuracy across three cities.

Demonstrated effective transfer learning with limited labeled data.

Enhanced detection performance using local feature aggregation.

Abstract

In this paper, we present an enhanced Convolutional Neural Network (CNN)-based rooftop solar photovoltaic (PV) panel detection approach using satellite images. We propose to use pre-trained CNN-based model to extract the local convolutional features of rooftops. These local features are then combined using the Vectors of Locally Aggregated Descriptors (VLAD) technique to obtain rooftop-level global features, which are then used to train traditional Machine Learning (ML) models to identify rooftop images that do and do not contain PV panels. On the dataset used in this study, the proposed approach achieved rooftop-PV classification scores exceeding the predefined threshold of 0.9 across all three cities for each of the feature extractor networks evaluated. Moreover, we propose a 3-phase approach to enable efficient utilization of the previously trained models on a new city or region with limited labelled data. We illustrate the effectiveness of this 3-phase approach for multi-city rooftop-PV detection task.