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 proceedingConference contributionpeer-review

10 Citations (Scopus)
42 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 languageEnglish
Title of host publicationIGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium
PublisherIEEE
Pages3231-3234
Number of pages4
ISBN (Electronic)978-1-6654-2792-0, 978-1-6654-2791-3
ISBN (Print)978-1-6654-2793-7
DOIs
Publication statusPublished (in print/issue) - 28 Sept 2022
Event2022 IEEE International Geoscience and Remote Sensing Symposium IGARSS. - Kuala Lumpur, Malaysia
Duration: 17 Jul 202222 Jul 2022

Publication series

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

Conference

Conference2022 IEEE International Geoscience and Remote Sensing Symposium IGARSS.
Country/TerritoryMalaysia
CityKuala Lumpur
Period17/07/2222/07/22

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

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