期刊名称：Environmental Health - a Global Access Science Source
印刷版ISSN：1476-069X
电子版ISSN：1476-069X
出版年度：2018
卷号：17
期号：1
页码：40
DOI：10.1186/s12940-018-0386-x
语种：English
出版社：BioMed Central
摘要：Geospatial artificial intelligence (geoAI) is an emerging scientific discipline that combines innovations in spatial science, artificial intelligence methods in machine learning (e.g., deep learning), data mining, and high-performance computing to extract knowledge from spatial big data. In environmental epidemiology, exposure modeling is a commonly used approach to conduct exposure assessment to determine the distribution of exposures in study populations. geoAI technologies provide important advantages for exposure modeling in environmental epidemiology, including the ability to incorporate large amounts of big spatial and temporal data in a variety of formats; computational efficiency; flexibility in algorithms and workflows to accommodate relevant characteristics of spatial (environmental) processes including spatial nonstationarity; and scalability to model other environmental exposures across different geographic areas. The objectives of this commentary are to provide an overview of key concepts surrounding the evolving and interdisciplinary field of geoAI including spatial data science, machine learning, deep learning, and data mining; recent geoAI applications in research; and potential future directions for geoAI in environmental epidemiology.
关键词：Geospatial artificial intelligence ; geoAI ; Spatial data science ; Machine learning ; Deep learning ; Data mining ; Remote sensing ; Environmental epidemiology ; Exposure modeling
