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: Contribution to conferencePaperpeer-review

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 use 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
Publication statusAccepted/In press - 5 Apr 2022
Event2022 IEEE International Geoscience and Remote Sensing Symposium IGARSS. - Kuala Lumpur, Malaysia
Duration: 17 Jul 202222 Jul 2022

Conference

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

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