测绘学报 ›› 2021, Vol. 50 ›› Issue (9): 1137-1146.doi: 10.11947/j.AGCS.2021.20200420

• 智能化测绘 •    下一篇

地理空间智能研究进展和面临的若干挑战

张永生, 张振超, 童晓冲, 纪松, 于英, 赖广陵   

  1. 信息工程大学地理空间信息学院, 河南 郑州 450001
  • 收稿日期:2020-08-31 修回日期:2021-06-08 发布日期:2021-10-09
  • 通讯作者: 张振超 E-mail:zhzhc_1@163.com
  • 作者简介:张永生(1963-),男,博士,教授,研究方向为摄影测量与遥感、地理空间智能等。E-mail:yszhang2001@vip.163.com
  • 基金资助:
    国防科技战略先导计划(19-ZLXD-06-17-01-800-01);遥感与空间智能系统中原学者首席科学家工作室专项(2018007)

Progress and challenges of geospatial artificial intelligence

ZHANG Yongsheng, ZHANG Zhenchao, TONG Xiaochong, JI Song, YU Ying, LAI Guangling   

  1. Institute of Geospatial Information, Information Engineering University, Zhengzhou 450001, China
  • Received:2020-08-31 Revised:2021-06-08 Published:2021-10-09
  • Supported by:
    The Science and Technology Strategic Pilot Program for National Defense (No. 19-ZLXD-06-17-01-800-01); The Chief Scientist Studio Program of Central Plain Scholar in Remote Sensing and Geospatial Intelligence System (No. 2018007)

摘要: 随着地理空间科学、人工智能、高性能计算技术的迅速发展,地理空间智能已成为处理和分析地理空间大数据的主要手段,并将在地球科学、空间认知、智慧城市、智慧社会等科学研究、工程建设和社会发展中发挥越来越重要的作用。地理空间智能作为地理空间科学和人工智能深度融合的交叉领域,其发展受到多学科的驱动,目前已在算力增强软硬件研制、系统开发、数据与模型共享、服务与应用方面不断取得进展,显示出巨大的活力和潜能,同时难题和挑战也相生相伴。本文首先阐述地理空间智能的概念演进、若干技术系统构建思路和国内外科学研究现状,然后梳理地理空间智能的典型应用,分析地理空间智能面临的问题和挑战,最后对其重要的发展方向及趋势予以展望。

关键词: 地理空间智能, 地理空间科学, 人工智能, 高性能计算, 深度学习

Abstract: With the rapid development of geospatial science, artificial intelligence, and high-performance computing, geospatial artificial intelligence has become the major technique for processing and analyzing geospatial big data, and will be widely used in the scientific research and engineering application of earth science, spatial cognition, and smart city. Geospatial intelligence, as an inter-discipline between geospatial science and artificial intelligence, is driven by dual disciplines. At present, it has made important progress in hardware research, system development, data and model sharing, services and applications, and is also facing new challenges. This paper first describes the conceptual evolution of geospatial artificial intelligence and lists the frameworks of some technical systems. The paper then reviews the state-of-the-art in the scientific research and typical application domains. The problems and challenges facing geospatial intelligence are analyzed. Finally, some future prospects and trends are presented.

Key words: geospatial artificial intelligence, geospatial science, artificial intelligence, high-performance computing, deep learning

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