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

Camilo, Joseph A.; Wang, Rui; Collins, Leslie M.; Bradbury, Kyle; Malof, Jordan M.

doi:10.48550/arxiv.1801.04018

Cited by 7 publications

(8 citation statements)

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“…With the development of convolutional neural network (CNN), many researchers begin to apply CNN to detecting PV facilities from satellite images. 3,4 The CNN approaches enable automatic representation learning and have the advantage of examining more complex spatial patterns that cannot be captured by shallow classifiers. 5 Therefore, they can significantly improve the accuracy of location and contour detection of PV facilities.…”

Section: Introductionmentioning

confidence: 99%

Automatic detection of photovoltaic facilities from Sentinel-2 observations by the enhanced U-Net method

Dui

Huang

Jin

et al. 2023

J. Appl. Rem. Sens.

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With the enactment of supportive government policies and the increasing maturity of solar photovoltaic (PV) technologies, solar PV energy has become the most cost-effective new energy resource worldwide. Geospatial information on existing solar PV power systems is necessary to manage and optimize the deployment of new PV facilities. In this study, we propose a new deep-learning network, named the enhanced U-Net (E-UNET), to detect PV facilities from Sentinel-2 multi-spectral remote sensing data. Our E-UNET features an enhanced encoderdecoder structure that can efficiently extract spectral and spatial features simultaneously by combining a multi-spectral three-dimensional convolution path and a multi-scale pooling block. We compare the performance of the E-UNET with other semantic segmentation deep-learning networks and a pixel-based random forest classifier. The experimental results show that the E-UNET performs better than the other methods. It achieves an overall accuracy, Matthews correlation coefficient, F1, kappa coefficient, and recall of 0.989, 0.862, 0.869, 0.934, and 0.875, respectively. The experimental results also indicate that the E-UNET accurately detects PV facilities from various complex environments with high accuracy in terms of PV integrity and details.

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Section: Introductionmentioning

confidence: 99%

Automatic detection of photovoltaic facilities from Sentinel-2 observations by the enhanced U-Net method

Dui

Huang

Jin

et al. 2023

J. Appl. Rem. Sens.

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“…Attention and ASPP modules were added on the basis of the Deeplabv3+ model, and the PointRend Network [18] was integrated to optimize the prediction boundary [19]. Camilo et al used SegNet [20] to extract distributed rooftop PVs based on remote sensing data from Fresno, California, USA [21]. Wani et al designed a lightweight distributed PV information extraction model based on Duke California Solar array (DCSA).…”

Section: Introductionmentioning

confidence: 99%

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

Tao,

He,

Wang

et al. 2023

Remote Sensing

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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.

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“…Many recent works have developed methods for solar panel detection [4,5,6,7,8]. One such study that produced notable advances in this area was DeepSolar, which was trained on over 350, 000 images resulting in a precision and recall for solar panel detection of 90% and a mean relative error of 2.1% for size estimation of solar panels [4].…”

Section: Introductionmentioning

confidence: 99%

HyperionSolarNet: Solar Panel Detection from Aerial Images

Poonam¹,

Sawasaki²,

Todeschini³

et al. 2022

Preprint

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With the effects of global climate change impacting the world, collective efforts are needed to reduce greenhouse gas emissions. The energy sector is the single largest contributor to climate change and many efforts are focused on reducing dependence on carbon-emitting power plants and moving to renewable energy sources, such as solar power. A comprehensive database of the location of solar panels is important to assist analysts and policymakers in defining strategies for further expansion of solar energy. In this paper we focus on creating a world map of solar panels. We identify locations and total surface area of solar panels within a given geographic area. We use deep learning methods for automated detection of solar panel locations and their surface area using aerial imagery. The framework, which consists of a two-branch model using an image classifier in tandem with a semantic segmentation model, is trained on our created dataset of satellite images. Our work provides an efficient and scalable method for detecting solar panels, achieving an accuracy of 0.96 for classification and an IoU score of 0.82 for segmentation performance.

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

Cited by 7 publications

References 0 publications

Automatic detection of photovoltaic facilities from Sentinel-2 observations by the enhanced U-Net method

Automatic detection of photovoltaic facilities from Sentinel-2 observations by the enhanced U-Net method

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

HyperionSolarNet: Solar Panel Detection from Aerial Images

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