Mapping the landscape and roadmap of geospatial artificial intelligence (GeoAI) in quantitative human geography: An extensive systematic review

地理空间分析 人文地理学 地理 数据科学 地理信息学 地理信息学 背景(考古学) 旅游 区域科学 地图学 计算机科学 经济地理学 考古
作者
Siqin Wang,Xiao Huang,Pengyuan Liu,Mengxi Zhang,Filip Biljecki,Tao Hu,Xiaokang Fu,Lingbo Liu,Xintao Liu,Ruomei Wang,Yuanyuan Huang,Jingjing Yan,Jinghan Jiang,Michaelmary Chukwu,Reza Naghedi,Moein Hemmati,Yaxiong Shao,Nan Jia,Zhiyang Xiao,Tian Tian
出处
期刊:International journal of applied earth observation and geoinformation 卷期号:128: 103734-103734 被引量:34
标识
DOI:10.1016/j.jag.2024.103734
摘要

This paper brings a comprehensive systematic review of the application of geospatial artificial intelligence (GeoAI) in quantitative human geography studies, including the subdomains of cultural, economic, political, historical, urban, population, social, health, rural, regional, tourism, behavioural, environmental and transport geography. In this extensive review, we obtain 14,537 papers from the Web of Science in the relevant fields and select 1516 papers that we identify as human geography studies using GeoAI via human scanning conducted by several research groups around the world. We outline the GeoAI applications in human geography by systematically summarising the number of publications over the years, empirical studies across countries, the categories of data sources used in GeoAI applications, and their modelling tasks across different subdomains. We find out that existing human geography studies have limited capacity to monitor complex human behaviour and examine the non-linear relationship between human behaviour and its potential drivers—such limits can be overcome by GeoAI models with the capacity to handle complexity. We elaborate on the current progress and status of GeoAI applications within each subdomain of human geography, point out the issues and challenges, as well as propose the directions and research opportunities for using GeoAI in future human geography studies in the context of sustainable and open science, generative AI, and quantum revolution.

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