Unsupervised land-use change detection using multi-temporal POI embedding-Reference-Cited by-同舟云学术

Unsupervised land-use change detection using multi-temporal POI embedding

Published:2023-09-26 Issue:11 Volume:37 Page:2392-2415
ISSN:1365-8816
Container-title:International Journal of Geographical Information Science
language:en
Short-container-title:International Journal of Geographical Information Science

Author:

Yao Yao¹²³^ORCID,Zhu Qia¹^ORCID,Guo Zijin¹^ORCID,Huang Weiming⁴^ORCID,Zhang Yatao⁵^ORCID,Yan Xiaoqin⁶^ORCID,Dong Anning¹^ORCID,Jiang Zhangwei⁷,Liu Hong⁷,Guan Qingfeng¹^ORCID

Affiliation:

1. School of Geography and Information Engineering, China University of Geosciences, Wuhan, Hubei Province, China

2. Center for Spatial Information Science, The University of Tokyo, Kashiwa-shi, Japan

3. Guangdong – Hong Kong – Macau Joint Laboratory for Smart Cities, Shenzhen, China

4. School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore

5. ETH Zurich, Future Resilient Systems, Singapore-ETH Centre, Singapore, Singapore

6. Institute of Remote Sensing and Geographical Information Systems, School of Earth and Space Sciences, Peking University, Beijing, China

7. Alibaba Group, Hangzhou, Zhejiang Province, China

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

“CUG Scholar” Scientific Research Funds at China University of Geosciences

Alibaba Innovative Research Project

Guangdong-Hong Kong-Macau Joint Laboratory Program

State Key Laboratory of Resources and Environmental Information System

Knut and Alice Wallenberg Foundation

Publisher

Informa UK Limited

Subject

Library and Information Sciences,Geography, Planning and Development,Information Systems

Link

https://www.tandfonline.com/doi/pdf/10.1080/13658816.2023.2257262

Reference43 articles.

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2. A deep learning framework for land-use/land-cover mapping and analysis using multispectral satellite imagery

3. Change detection techniques for remote sensing applications: a survey

4. Geographic mapping with unsupervised multi-modal representation learning from VHR images and POIs

5. Belkin M. and Niyogi P. 2001. Laplacian eigenmaps and spectral techniques for embedding and clustering. NIPS'01: proceedings of the 14th international conference on neural information processing systems: natural and synthetic 585–591. ACM.

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