Predicting the locations of missing persons in China by using NGO data and deep learning techniques-Reference-Cited by-同舟云学术

Predicting the locations of missing persons in China by using NGO data and deep learning techniques

Published:2024-01-17 Issue:1 Volume:17 Page:
ISSN:1753-8947
Container-title:International Journal of Digital Earth
language:en
Short-container-title:International Journal of Digital Earth

Author:

Dong Anning¹,Zhang Yatao²,Guo Zijin¹,Luo Peng³,Yao Yao¹⁴^ORCID,He Jialyu⁵,Zhu Qia¹,Jiang Ying¹,Xiong Kailu¹,Guan Qingfeng¹

Affiliation:

1. School of Geography and Information Engineering, China University of Geosciences, Wuhan, People’s Republic of China

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

3. Chair of Cartography and Visual Analytics, Technical University of Munich, Munich, Germany

4. Center for Spatial Information Science, The University of Tokyo, Chiba, Japan

5. Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hung Hom, Hong Kong

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 (Wuhan

)

Key Laboratory of Earth Surface System and Human-Earth Relations, Ministry of Natural Resources of China

Guangdong-Hong Kong-Macau Joint Laboratory Program

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/17538947.2024.2304076

Reference41 articles.

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2. The Role of Verbs in Sentence Production

3. Eyewitness identification in actual criminal cases: An archival analysis.

4. Sample size planning for classification models

5. A neural classification method for supporting the creation of BioVerbNet

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