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Article

Urban Green Plastic Cover Mapping Based on VHR Remote Sensing Images and a Deep Semi-Supervised Learning Framework

1
School of Surveying and Geo-Informatics, Shandong Jianzhu University, Jinan 250101, China
2
College of Land Science and Technology, China Agricultural University, Beijing 100083, China
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Key Laboratory of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China
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University of Chinese Academy of Sciences, Beijing 100049, China
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Institute of Geography and Geoecology, Mongolian Academy of Sciences, Ulaanbaatar 15170, Mongolia
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National Engineering Research Center for Geoinformatics, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
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School of Information Engineering, China University of Geosciences (Beijing), Beijing 100083, China
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2020, 9(9), 527; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9090527
Received: 10 August 2020 / Revised: 28 August 2020 / Accepted: 31 August 2020 / Published: 2 September 2020
(This article belongs to the Special Issue Measuring, Mapping, Modeling, and Visualization of Cities)
With the rapid process of both urban sprawl and urban renewal, large numbers of old buildings have been demolished in China, leading to wide spread construction sites, which could cause severe dust contamination. To alleviate the accompanied dust pollution, green plastic mulch has been widely used by local governments of China. Therefore, timely and accurate mapping of urban green plastic covered regions is of great significance to both urban environmental management and the understanding of urban growth status. However, the complex spatial patterns of the urban landscape make it challenging to accurately identify these areas of green plastic cover. To tackle this issue, we propose a deep semi-supervised learning framework for green plastic cover mapping using very high resolution (VHR) remote sensing imagery. Specifically, a multi-scale deformable convolution neural network (CNN) was exploited to learn representative and discriminative features under complex urban landscapes. Afterwards, a semi-supervised learning strategy was proposed to integrate the limited labeled data and massive unlabeled data for model co-training. Experimental results indicate that the proposed method could accurately identify green plastic-covered regions in Jinan with an overall accuracy (OA) of 91.63%. An ablation study indicated that, compared with supervised learning, the semi-supervised learning strategy in this study could increase the OA by 6.38%. Moreover, the multi-scale deformable CNN outperforms several classic CNN models in the computer vision field. The proposed method is the first attempt to map urban green plastic-covered regions based on deep learning, which could serve as a baseline and useful reference for future research. View Full-Text
Keywords: green plastic cover; semi-supervised learning; deep learning; urban land cover mapping green plastic cover; semi-supervised learning; deep learning; urban land cover mapping
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MDPI and ACS Style

Liu, J.; Feng, Q.; Wang, Y.; Batsaikhan, B.; Gong, J.; Li, Y.; Liu, C.; Ma, Y. Urban Green Plastic Cover Mapping Based on VHR Remote Sensing Images and a Deep Semi-Supervised Learning Framework. ISPRS Int. J. Geo-Inf. 2020, 9, 527. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9090527

AMA Style

Liu J, Feng Q, Wang Y, Batsaikhan B, Gong J, Li Y, Liu C, Ma Y. Urban Green Plastic Cover Mapping Based on VHR Remote Sensing Images and a Deep Semi-Supervised Learning Framework. ISPRS International Journal of Geo-Information. 2020; 9(9):527. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9090527

Chicago/Turabian Style

Liu, Jiantao; Feng, Quanlong; Wang, Ying; Batsaikhan, Bayartungalag; Gong, Jianhua; Li, Yi; Liu, Chunting; Ma, Yin. 2020. "Urban Green Plastic Cover Mapping Based on VHR Remote Sensing Images and a Deep Semi-Supervised Learning Framework" ISPRS Int. J. Geo-Inf. 9, no. 9: 527. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9090527

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