Urban Green Plastic Cover Mapping Based on VHR Remote Sensing Images and a Deep Semi-Supervised Learning Framework
Bibliographic Data
| ID | 22033480 |
|---|---|
| Authors | Jiantao Liu (0000-0001-5836-5641, Shandong Jianzhu University), Quanlong Feng (0000-0002-0569-4131, China Agricultural University, corresponding author), Ying Wang (0000-0003-3126-1134, Chinese Academy of Sciences), Bayartungalag Batsaikhan (0000-0002-4296-4225, Mongolian Academy of Sciences), Jianhua Gong (0000-0002-2577-1309, Chinese Academy of Sciences), Yi Li (0000-0002-3875-3513, Chinese Academy of Sciences), Chunting Liu (0000-0002-4628-5211, Shandong Jianzhu University), Yin Ma (0000-0001-9064-9361, China University of Geosciences (Beijing)) |
| Year | 2020 |
| Volume | 9 |
| Issue | 9 |
| Pages | 527 |
| Publication date | 2020-09-02 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | ISPRS International Journal of Geo-Information (JOURNAL) |
| Journal identifiers | ISSN: 2220-9964 • E-ISSN: 2220-9964 |
| Publisher | MDPI AG (PUBLISHER • IT) |
| DOI | 10.3390/ijgi9090527 |
| OpenAlex | W3082733530 |
| Language | EN |
| References cited | 42 |
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
Cartography · Civil engineering · Convolutional neural network · Deep learning · Discriminative model · Geography · Land cover · Land use · Remote sensing · Urban planning · Urban sprawl · Automated Road and Building Extraction · Computer Science · Engineering · Environmental Science · Land Use and Ecosystem Services · Remote-Sensing Image Classification · Artificial Intelligence
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The Regression Analysis of Binary Sequences
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| Highly cited | No |
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