An Information Extraction Method for Industrial and Commercial Rooftop Photovoltaics Based on GaoFen-7 Remote Sensing Images

Abstract

With the increasing global focus on renewable energy, distributed rooftop photovoltaics (PVs) are gradually becoming an important form of energy generation. Effective monitoring of rooftop PV information can obtain their spatial distribution and installed capacity, which is the basis used by management departments to formulate regulatory policies. Due to the time-consuming and labor-intensive problems involved in manual monitoring, remote-sensing-based monitoring methods are getting more attention. Currently, remote-sensing-based distributed rooftop PV monitoring methods are mainly used as household rooftop PVs, and most of them use aerial or satellite images with a resolution higher than 0.3 m; there is no research on industrial and commercial rooftop PVs. This study focuses on the distributed industrial and commercial rooftop PV information extraction method based on the Gaofen-7 satellite with a resolution of 0.65 m. First, the distributed industrial and commercial rooftop PV dataset based on Gaofen-7 satellite and the optimized public PV datasets were constructed. Second, an advanced MANet model was proposed. Compared to MANet, the proposed model removed the downsample operation in the first stage of the encoder and added an auxiliary branch containing the Atrous Spatial Pyramid Pooling (ASPP) module in the decoder. Comparative experiments were conducted between the advanced MANet and state-of-the-art semantic segmentation models. In the Gaofen-7 satellite PV dataset, the Intersection over Union (IoU) of the advanced MANet in the test set was improved by 13.5%, 8.96%, 2.67%, 0.63%, and 0.75% over Deeplabv3+, U2net-lite, U2net-full, Unet, and MANet. In order to further verify the performance of the proposed model, experiments were conducted on optimized public PV datasets. The IoU was improved by 3.18%, 3.78%, 3.29%, 4.98%, and 0.42%, demonstrating that it outperformed the other models.