高松. 地理空间人工智能的近期研究总结与思考[J]. 武汉大学学报 ( 信息科学版), 2020, 45(12): 1865-1874. DOI: 10.13203/j.whugis20200597
引用本文: 高松. 地理空间人工智能的近期研究总结与思考[J]. 武汉大学学报 ( 信息科学版), 2020, 45(12): 1865-1874. DOI: 10.13203/j.whugis20200597
GAO Song. A Review of Recent Researches and Reflections on Geospatial Artificial Intelligence[J]. Geomatics and Information Science of Wuhan University, 2020, 45(12): 1865-1874. DOI: 10.13203/j.whugis20200597
Citation: GAO Song. A Review of Recent Researches and Reflections on Geospatial Artificial Intelligence[J]. Geomatics and Information Science of Wuhan University, 2020, 45(12): 1865-1874. DOI: 10.13203/j.whugis20200597

地理空间人工智能的近期研究总结与思考

A Review of Recent Researches and Reflections on Geospatial Artificial Intelligence

  • 摘要: 人工智能领域的技术进步给地理空间相关领域研究的智能化发展和融合创新带来了新机遇和新挑战。地理空间人工智能(geospatial artificial intelligence,GeoAI)是指地理空间科学与人工智能相结合的交叉学科研究方向,通过研究与开发机器的空间智能,提升对于地理现象和地球科学过程的动态感知、智能推理和知识发现能力,并寻求解决人类和地球环境系统相互作用中的重大科学和工程问题。简要回顾了GeoAI发展的历史渊源,介绍空间显式与隐式的人工智能模型,总结近期研究热点话题和应用方向(包括空间表征学习、时空预测和空间插值、对地资源环境监测、地图学、地理文本语义分析),思考并提出地理空间人工智能未来发展的几个重要挑战和研究方向。

     

    Abstract: The technological progress in the field of artificial intelligence (AI) has brought new opportunities and challenges to the intelligent development and innovative research in geospatial related fields. Geospatial artificial intelligence (GeoAI) refers to the interdisciplinary research direction that combines geography, earth science and artificial intelligence, and seeks to solve major scientific and engineering problems in human-environmental interaction systems through the research and development of spatial intelligence in machines to improve the dynamic perception, intelligent reasoning and knowledge discovery of geographic phenomena and earth science processes. This paper briefly summarizes the historical origins of GeoAI development, introduces spatially explicit and implicit AI models, reviews recent GeoAI research and applications(including spatial representation learning, spatiotemporal prediction and spatial interpolation, monitoring of geographic resources and environment, cartography, and geo-text data semantic analysis), and identifies several potential research challenges and directions for the future development of GeoAI.

     

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