Photovoltaic Installations Change Detection from Remote Sensing Images Using Deep Learning

Kaiyuan Shi; Lu Bai; Zhibao Wang; Xifeng Tong; Maurice Mulvenna; RR Bond

doi:10.1109/igarss46834.2022.9883738

Photovoltaic Installations Change Detection from Remote Sensing Images Using Deep Learning

Kaiyuan Shi, Lu Bai, Zhibao Wang, Xifeng Tong, Maurice Mulvenna, RR Bond

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

12 Citations (Scopus)

48 Downloads (Pure)

Abstract

The development and monitoring of Photovoltaic (PV) installations is of great interests for the Chinese energy management agency in recent years. The traditional land change detection of PV installations has issues pertaining to low efficiency and high missed detection rates. Therefore, this paper explores an efficient and high accurate detection method of PV installations land using changes from remote sensing images in order to help relevant stakeholders to better manage and monitor urban energy and environment. In this paper, Full Convolutional Network (FCN) and classical segmentation convolutional network (U-Net) based deep learning algorithms are used to build change detection models. To evaluate the model performance, we have built the change detection dataset from Northeast Petroleum University-Photovoltaic Remote Sensing Dataset (NEPU-PRSD) of PV installations in Western China. The experimental results show that both models can achieve good accuracy in change detection regarding PV installations.

Original language	English
Title of host publication	IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium
Publisher	IEEE
Pages	3231-3234
Number of pages	4
ISBN (Electronic)	978-1-6654-2792-0, 978-1-6654-2791-3
ISBN (Print)	978-1-6654-2793-7
DOIs	https://doi.org/10.1109/igarss46834.2022.9883738
Publication status	Published (in print/issue) - 28 Sept 2022
Event	2022 IEEE International Geoscience and Remote Sensing Symposium IGARSS. - Kuala Lumpur, Malaysia Duration: 17 Jul 2022 → 22 Jul 2022

Publication series

Name
ISSN (Print)	2153-6996
ISSN (Electronic)	2153-7003

Conference

Conference	2022 IEEE International Geoscience and Remote Sensing Symposium IGARSS.
Country/Territory	Malaysia
City	Kuala Lumpur
Period	17/07/22 → 22/07/22

Bibliographical note

Publisher Copyright:
© 2022 IEEE.

Keywords

Remote sensing
change detection
U-Net
full convolutional network
deep learning
convolutional neural network
photovoltaic systems
image segmentation
roads
neural networks
industrial plants
stakeholders

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/igarss46834.2022.9883738

Shi_igarss_2022_accepted_manuscriptAuthor Accepted Manuscript, 988 KBLicence: Unspecified

Cite this

@inproceedings{f39f3d9e80cd47a49c5836e96106df22,

title = "Photovoltaic Installations Change Detection from Remote Sensing Images Using Deep Learning",

abstract = "The development and monitoring of Photovoltaic (PV) installations is of great interests for the Chinese energy management agency in recent years. The traditional land change detection of PV installations has issues pertaining to low efficiency and high missed detection rates. Therefore, this paper explores an efficient and high accurate detection method of PV installations land using changes from remote sensing images in order to help relevant stakeholders to better manage and monitor urban energy and environment. In this paper, Full Convolutional Network (FCN) and classical segmentation convolutional network (U-Net) based deep learning algorithms are used to build change detection models. To evaluate the model performance, we have built the change detection dataset from Northeast Petroleum University-Photovoltaic Remote Sensing Dataset (NEPU-PRSD) of PV installations in Western China. The experimental results show that both models can achieve good accuracy in change detection regarding PV installations.",

keywords = "Remote sensing, change detection, U-Net, full convolutional network, deep learning, convolutional neural network, photovoltaic systems, image segmentation, roads, neural networks, industrial plants, stakeholders",

author = "Kaiyuan Shi and Lu Bai and Zhibao Wang and Xifeng Tong and Maurice Mulvenna and RR Bond",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE International Geoscience and Remote Sensing Symposium IGARSS. ; Conference date: 17-07-2022 Through 22-07-2022",

year = "2022",

month = sep,

day = "28",

doi = "10.1109/igarss46834.2022.9883738",

language = "English",

isbn = "978-1-6654-2793-7",

publisher = "IEEE",

pages = "3231--3234",

booktitle = "IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium",

address = "United States",

}

Shi, K, Bai, L, Wang, Z, Tong, X, Mulvenna, M & Bond, RR 2022, Photovoltaic Installations Change Detection from Remote Sensing Images Using Deep Learning. in IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium. IEEE, pp. 3231-3234, 2022 IEEE International Geoscience and Remote Sensing Symposium IGARSS., Kuala Lumpur, Malaysia, 17/07/22. https://doi.org/10.1109/igarss46834.2022.9883738

TY - GEN

T1 - Photovoltaic Installations Change Detection from Remote Sensing Images Using Deep Learning

AU - Shi, Kaiyuan

AU - Bai, Lu

AU - Wang, Zhibao

AU - Tong, Xifeng

AU - Mulvenna, Maurice

AU - Bond, RR

PY - 2022/9/28

Y1 - 2022/9/28

N2 - The development and monitoring of Photovoltaic (PV) installations is of great interests for the Chinese energy management agency in recent years. The traditional land change detection of PV installations has issues pertaining to low efficiency and high missed detection rates. Therefore, this paper explores an efficient and high accurate detection method of PV installations land using changes from remote sensing images in order to help relevant stakeholders to better manage and monitor urban energy and environment. In this paper, Full Convolutional Network (FCN) and classical segmentation convolutional network (U-Net) based deep learning algorithms are used to build change detection models. To evaluate the model performance, we have built the change detection dataset from Northeast Petroleum University-Photovoltaic Remote Sensing Dataset (NEPU-PRSD) of PV installations in Western China. The experimental results show that both models can achieve good accuracy in change detection regarding PV installations.

AB - The development and monitoring of Photovoltaic (PV) installations is of great interests for the Chinese energy management agency in recent years. The traditional land change detection of PV installations has issues pertaining to low efficiency and high missed detection rates. Therefore, this paper explores an efficient and high accurate detection method of PV installations land using changes from remote sensing images in order to help relevant stakeholders to better manage and monitor urban energy and environment. In this paper, Full Convolutional Network (FCN) and classical segmentation convolutional network (U-Net) based deep learning algorithms are used to build change detection models. To evaluate the model performance, we have built the change detection dataset from Northeast Petroleum University-Photovoltaic Remote Sensing Dataset (NEPU-PRSD) of PV installations in Western China. The experimental results show that both models can achieve good accuracy in change detection regarding PV installations.

KW - Remote sensing

KW - change detection

KW - U-Net

KW - full convolutional network

KW - deep learning

KW - convolutional neural network

KW - photovoltaic systems

KW - image segmentation

KW - roads

KW - neural networks

KW - industrial plants

KW - stakeholders

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U2 - 10.1109/igarss46834.2022.9883738

DO - 10.1109/igarss46834.2022.9883738

M3 - Conference contribution

SN - 978-1-6654-2793-7

SP - 3231

EP - 3234

BT - IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium

PB - IEEE

T2 - 2022 IEEE International Geoscience and Remote Sensing Symposium IGARSS.

Y2 - 17 July 2022 through 22 July 2022

ER -

Photovoltaic Installations Change Detection from Remote Sensing Images Using Deep Learning

Abstract

Publication series

Conference

Bibliographical note

Keywords

UN SDGs

Access to Document

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