地球信息科学学报 ›› 2020, Vol. 22 ›› Issue (1): 76-87.doi: 10.12082/dqxxkx.2020.190701

• 专辑:地理智能 • 上一篇    下一篇

人工智能GIS软件技术体系初探

宋关福1,2,3,*(), 卢浩1,2, 王晨亮3, 胡辰璞1,2, 黄科佳1,2   

  1. 1. 北京超图软件股份有限公司,北京 100015
    2. 自然资源部地理信息系统技术创新中心,北京 100015
    3. 中国科学院地理科学与资源研究所,北京 100101
  • 收稿日期:2019-11-20 修回日期:2020-01-03 出版日期:2020-01-25 发布日期:2020-04-08
  • 通讯作者: 宋关福 E-mail:songguanfu@supermap.com
  • 作者简介:宋关福(1969— ),男,博士,重庆人,教授级高工,主要从事地理信息软件技术研究。
  • 基金资助:
    国家重点研发计划项目(2018YFB2100700);国家重点研发计划项目(2017YFD0300403)

A Tentative Study on System of Software Technology for Artificial Intelligence GIS

SONG Guanfu1,2,3,*(), LU Hao1,2, WANG Chenliang3, HU Chenpu1,2, HUANG Kejia1,2   

  1. 1. SuperMap Software Company Limited, Beijing 100015, China
    2. GIS Technology Innovation Center of Ministry of Natural Resources, Beijing 100015, China
    3. Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
  • Received:2019-11-20 Revised:2020-01-03 Online:2020-01-25 Published:2020-04-08
  • Contact: SONG Guanfu E-mail:songguanfu@supermap.com
  • Supported by:
    National Key Research and Development Program of China(2018YFB2100700);National Key Research and Development Program of China(2017YFD0300403)

摘要:

作为人工智能的代表性技术,深度学习已经成为大数据等各个领域中最具有突破性发展的新技术。深度学习的成功主要得益于其新颖的数据驱动的特征表示学习能力,这种能力成功地替代了传统建模中基于领域知识人为设计特征的方式。在这些技术推动下,人工智能技术在新一代GIS基础软件技术的研究与应用中发挥着极为重要的作用,而现有人工智能GIS(AI GIS)技术研究整体仍处于初步探索阶段,距离成熟阶段尚有较大距离。作为新一代GIS基础软件的方法和技术,AI GIS已经广泛应用在遥感数据分析、水资源研究、空间流行病学和环境健康等地学领域,与传统GIS模型相比大大提高了对非结构化的遥感或街景影像和文本的地理信息提取和特征理解能力,显示出巨大的价值和发展潜力,但现有研究对AI GIS软件技术体系的梳理和总结尚不够全面。大部分研究只关注地理空间人工智能算法的研究及其特定场景下的应用研究,而对相关的AI GIS软件技术体系关注较少。本文分析了地理智慧的几个层次,并讨论了其与AI GIS的关系,总体介绍了国内外现有人工智能技术与GIS软件相结合的发展现状,进而提出了AI GIS软件技术体系。根据AI与GIS的结合关系提出了AI GIS由地理空间智能算法、AI赋能GIS和GIS赋能AI三部分组成。此外,为深入介绍AI GIS各部分组成,本文以SuperMap为例,探讨了AI GIS软件的设计与实现。最后,探讨了AI GIS的未来发展中亟需解决的问题。本文基于AI GIS软件技术的初步探索,尝试为地理智能的基础GIS软件技术体系的构建提供理论基础,以促进人工智能技术与GIS技术的进一步融合和发展,为实现地理智能提供一个可行的研究方向。

关键词: 人工智能, GIS软件技术, 地理智慧, AI GIS技术体系, 空间深度学习, 空间机器学习, AI流程工具

Abstract:

As the representative technology of Artificial Intelligence, deep learning has been the most exciting breakthrough technologies in big data analysis and other domains researches due to its novel data-driven feature representations learning, instead of handcrafting features based on domain-specific knowledge in traditional modeling. Driven by these technological developments. Artificial Intelligence plays a key role in the researches and applications of next-generation geographical information system software technology. Nevertheless, most researches about AI GIS are still in the stage of immature and preliminary exploration. As a method and technology for the novel architecture of GIS fundamental software, AI GIS is widely used in many earth science applications including remote sensing data analysis, water resources research, spatial epidemiology and environmental health. All these technologies are significantly improving capabilities of data processing of traditional GIS, and being able to extract geospatial information and characteristics from unstructured datasets such as street view or remote sensing imagery, texts. These applications are showing great value and developing potential of AI GIS. However, the existing research on the system of software technology of AI GIS is not comprehensive enough. A variety of AI GIS algorithms or models and their scenario-specific applications are commonly considered to be the most important topic. Few researchers have addressed the issues or theory of Artificial Intelligence GIS technologies system and software architecture. This paper presents and analyzes several levels of Geo-intelligence and discuss its relationships to AI GIS technology system , reviewed the research status in AI and GIS technologies from the domestic and abroad perspectives. Then, the system of software technology of AI GIS is proposed according to the relationships between Artificial Intelligence and GIS. This paper define the architecture of AI GIS into three parts including Geospatial Artificial Intelligence(GeoAI), AI for GIS, and GIS for AI. And concepts and examples for each parts of Artificial Intelligence GIS are also analyzed for illustration. Furthermore, in order to deeply explain and investigate the AI GIS software technologies architecture, this paper provide the example of the design and implementation of SuperMap AI GIS software architectures and production. Finally, this paper discusses the problems that need to be solved in the future development of GIS. The tentative study of AI GIS in this paper may provide a theory for establishing the fundamental GIS software technology architecture of Geo-intelligence, which would helps to promote the deep integration and development of AI and GIS technology, and make suggestions for further research about Geo-intelligence.

Key words: artificial intelligence, software technology of GIS, Geo-intelligence, AI GIS technology architecture, geospatial deep learning, geospatial machine learning, AI pipeline toolkits