Photovoltaic installations change detection from remote sensing images using deep learning

Kaiyuan Shi; Lu Bai; Zhibao Wang; Xifeng Tong; Maurice D. Mulvenna; Raymond R. 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 D. Mulvenna, Raymond R. Bond

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

13 Citations (Scopus)

51 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: proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	3231-3234
Number of pages	4
ISBN (Electronic)	9781665427920
ISBN (Print)	9781665427937
DOIs	https://doi.org/10.1109/IGARSS46834.2022.9883738
Publication status	Published - 28 Sept 2022
Externally published	Yes
Event	IEEE International Geoscience and Remote Sensing Symposium 2022 - Kuala Lumpur, Malaysia Duration: 17 Jul 2022 → 22 Jul 2022

Publication series

Name	International Geoscience and Remote Sensing Symposium (IGARSS)
Volume	2022-July
ISSN (Print)	2153-6996
ISSN (Electronic)	2153-7003

Conference

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

Bibliographical note

Publisher Copyright:
© 2022 IEEE.

Keywords

change detection
convolutional neural network
deep learning
full convolutional network
Remote sensing
U-Net

ASJC Scopus subject areas

Computer Science Applications
General Earth and Planetary Sciences

UN SDGs

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

Access to Document

10.1109/IGARSS46834.2022.9883738Licence: Unspecified

Photovoltaic installations change detection from remote sensing images using deep learning
Copyright 2022 IEEE. This work is made available online in accordance with the publisher’s policies. Please refer to any applicable terms of use of the publisher.
Accepted author manuscript, 435 KBLicence: Unspecified

Cite this

Shi, K., Bai, L., Wang, Z., Tong, X., Mulvenna, M. D., & Bond, R. R. (2022). Photovoltaic installations change detection from remote sensing images using deep learning. In IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium: proceedings (pp. 3231-3234). (International Geoscience and Remote Sensing Symposium (IGARSS); Vol. 2022-July). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IGARSS46834.2022.9883738

Shi, Kaiyuan ; Bai, Lu ; Wang, Zhibao et al. / Photovoltaic installations change detection from remote sensing images using deep learning. IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium: proceedings. Institute of Electrical and Electronics Engineers Inc., 2022. pp. 3231-3234 (International Geoscience and Remote Sensing Symposium (IGARSS)).

@inproceedings{5f962474b06548dda17679caa80f441c,

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 = "change detection, convolutional neural network, deep learning, full convolutional network, Remote sensing, U-Net",

author = "Kaiyuan Shi and Lu Bai and Zhibao Wang and Xifeng Tong and Mulvenna, {Maurice D.} and Bond, {Raymond R.}",

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

year = "2022",

month = sep,

day = "28",

doi = "10.1109/IGARSS46834.2022.9883738",

language = "English",

isbn = "9781665427937",

series = "International Geoscience and Remote Sensing Symposium (IGARSS)",

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Shi, K, Bai, L, Wang, Z, Tong, X, Mulvenna, MD & 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: proceedings. International Geoscience and Remote Sensing Symposium (IGARSS), vol. 2022-July, Institute of Electrical and Electronics Engineers Inc., pp. 3231-3234, IEEE International Geoscience and Remote Sensing Symposium 2022, Kuala Lumpur, Malaysia, 17/07/2022. https://doi.org/10.1109/IGARSS46834.2022.9883738

Photovoltaic installations change detection from remote sensing images using deep learning. / Shi, Kaiyuan; Bai, Lu; Wang, Zhibao et al.
IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium: proceedings. Institute of Electrical and Electronics Engineers Inc., 2022. p. 3231-3234 (International Geoscience and Remote Sensing Symposium (IGARSS); Vol. 2022-July).

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

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 D.

AU - Bond, Raymond R.

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.

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KW - full convolutional network

KW - Remote sensing

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

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ER -

Shi K, Bai L, Wang Z, Tong X, Mulvenna MD, Bond RR. Photovoltaic installations change detection from remote sensing images using deep learning. In IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium: proceedings. Institute of Electrical and Electronics Engineers Inc. 2022. p. 3231-3234. (International Geoscience and Remote Sensing Symposium (IGARSS)). doi: 10.1109/IGARSS46834.2022.9883738

Photovoltaic installations change detection from remote sensing images using deep learning

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

UN SDGs

Access to Document

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Cite this