Learning spatial interaction representation with heterogeneous graph convolutional networks for urban land-use inference

IF 4.3 1区地球科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

International Journal of Geographical Information Science Pub Date : 2024-07-22 DOI:10.1080/13658816.2024.2379473

Zhaoya Gong, Chenglong Wang, Yuting Chen, Bin Liu, Pengjun Zhao, Zhengzi Zhou

引用次数: 0

Abstract

Urban land use is central to urban planning. With the emergence of urban big data and advances in deep learning methods, several studies have leveraged graph convolutional networks (GCNs) with loca...

查看原文本刊更多论文

利用异构图卷积网络学习空间交互表征，用于城市土地利用推断

城市土地利用是城市规划的核心。随着城市大数据的出现和深度学习方法的进步，一些研究利用图卷积网络（GCNs）的定位功能来分析城市土地使用情况。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

International Journal of Geographical Information Science 地学-计算机：信息系统

CiteScore

11.00

自引率

7.00%

发文量

审稿时长

9 months

期刊介绍： International Journal of Geographical Information Science provides a forum for the exchange of original ideas, approaches, methods and experiences in the rapidly growing field of geographical information science (GIScience). It is intended to interest those who research fundamental and computational issues of geographic information, as well as issues related to the design, implementation and use of geographical information for monitoring, prediction and decision making. Published research covers innovations in GIScience and novel applications of GIScience in natural resources, social systems and the built environment, as well as relevant developments in computer science, cartography, surveying, geography and engineering in both developed and developing countries.