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Article

Automatic Updating of Land Cover Maps in Rapidly Urbanizing Regions by Relational Knowledge Transferring from GlobeLand30

by 1,2,3, 1,2,3,*, 4, 5, 1,2,3 and 6
1
School of Geography and Ocean Science, Nanjing University, Nanjing 210093, China
2
Key Laboratory for Satellite Mapping Technology and Application of National Administration of Surveying, Mapping and Geoinformation of China, Nanjing University, Nanjing 210093, China
3
Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Nanjing University, Nanjing 210093, China
4
State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Ürümqi 830011, China
5
School of Geography, Geomatics and Planning, Jiangsu Normal University, Xuzhou 221116, China
6
Geoinformatics Unit, RIKEN Center for Advanced Intelligence Project, Tokyo 103-0027, Japan
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(12), 1397; https://doi.org/10.3390/rs11121397
Received: 30 April 2019 / Revised: 29 May 2019 / Accepted: 5 June 2019 / Published: 12 June 2019
(This article belongs to the Special Issue Operational Land Cover/Land Use Mapping)
Land-cover map is the basis of research and application related to urban planning, environmental management and ecological protection. Land-cover updating is an essential task especially in a rapidly urbanizing region, where fast development makes it necessary to monitor land-cover change in a timely manner. However, conventional approaches always have the limitations of large amounts of sample collection and exploitation of relational knowledge between multi-modality remote sensing datasets. With some global land-cover products being available, it is important to produce new land-cover maps based on the existing land-cover products and time series images. To this end, a novel transfer learning based automatic approach was proposed for updating land cover maps of rapidly urbanizing regions. In detail, the proposed method is composed of the following three steps. The first is to design a strategy to extract reliable land-cover information from the historical land-cover map for one of the images (source domain). Then, a novel relational knowledge transfer technique is applied to transfer label information. Finally, classifiers are trained on the transferred samples with spatio-spectral features. The experimental results show that aforementioned steps can select sufficient effective samples for target images, and for the main land-cover classes in a rapidly urbanizing region; the results of an updated map show good performance in both precision and vision. Therefore, the proposed approach provides an automatic solution for urban land-cover mapping with a high degree of accuracy. View Full-Text
Keywords: land-cover updating; rapidly urbanizing regions; transfer learning; automatic image classification land-cover updating; rapidly urbanizing regions; transfer learning; automatic image classification
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MDPI and ACS Style

Lin, C.; Du, P.; Samat, A.; Li, E.; Wang, X.; Xia, J. Automatic Updating of Land Cover Maps in Rapidly Urbanizing Regions by Relational Knowledge Transferring from GlobeLand30. Remote Sens. 2019, 11, 1397. https://doi.org/10.3390/rs11121397

AMA Style

Lin C, Du P, Samat A, Li E, Wang X, Xia J. Automatic Updating of Land Cover Maps in Rapidly Urbanizing Regions by Relational Knowledge Transferring from GlobeLand30. Remote Sensing. 2019; 11(12):1397. https://doi.org/10.3390/rs11121397

Chicago/Turabian Style

Lin, Cong, Peijun Du, Alim Samat, Erzhu Li, Xin Wang, and Junshi Xia. 2019. "Automatic Updating of Land Cover Maps in Rapidly Urbanizing Regions by Relational Knowledge Transferring from GlobeLand30" Remote Sensing 11, no. 12: 1397. https://doi.org/10.3390/rs11121397

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