利用非政府组织数据和深度学习技术预测中国失踪人员的位置

IF 3.7 1区地球科学 Q1 GEOGRAPHY, PHYSICAL

International Journal of Digital Earth Pub Date : 2024-01-17 DOI:10.1080/17538947.2024.2304076

Anning Dong, Yatao Zhang, Zijin Guo, Peng Luo, Yao Yao, Jialyu He, Qia Zhu, Ying Jiang, Kailu Xiong, Qingfeng Guan

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引用次数: 0

摘要

失踪人口犯罪会严重影响中国家庭的幸福，而失踪人口目的地预测有助于解决这一问题。利用非政府组织（NGO）的数据来预测失踪人员的去向，可以帮助解决这个问题。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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

Missing person crimes can seriously affect the well-being of Chinese families, and missing person destination prediction can help to solve this problem. Using nongovernmental organization (NGO) dat...

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来源期刊

International Journal of Digital Earth 地学-遥感

CiteScore

6.50

自引率

3.90%

发文量

审稿时长

3 months

期刊介绍： The International Journal of Digital Earth is a response to this initiative. This peer-reviewed academic journal (SCI-E) focuses on the theories, technologies, applications, and societal implications of Digital Earth and those visionary concepts that will enable a modeled virtual world. The journal encourages papers that: Progress visions for Digital Earth frameworks, policies, and standards; Explore geographically referenced 3D, 4D, or 5D models to represent the real planet, and geo-data-intensive science and discovery; Develop methods that turn all forms of geo-referenced data, from scientific to social, into useful information that can be analyzed, visualized, and shared; Present innovative, operational applications and pilots of Digital Earth technologies at a local, national, regional, and global level; Expand the role of Digital Earth in the fields of Earth science, including climate change, adaptation and health related issues,natural disasters, new energy sources, agricultural and food security, and urban planning; Foster the use of web-based public-domain platforms, social networks, and location-based services for the sharing of digital data, models, and information about the virtual Earth; and Explore the role of social media and citizen-provided data in generating geo-referenced information in the spatial sciences and technologies.