| 综述与展望：地理空间数据的管理、多尺度变换与表达 |
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| 引用本文： | 王迪,钱海忠,赵钰哲.综述与展望：地理空间数据的管理、多尺度变换与表达[J].地球信息科学,2022,24(12):2265-2281. |
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| 作者姓名： | 王迪 钱海忠 赵钰哲 |
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| 作者单位： | 信息工程大学地理空间信息学院，郑州 450000 |
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| 基金项目： | 国家自然科学基金项目(42271463);河南省杰出青年科学基金项目(212300410014) |
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| 摘 要： | 多尺度表达是地理空间数据的重要研究内容之一。本文从地理空间数据管理、地理空间数据尺度变换以及地图多尺度表达3个方面对地理空间数据的多尺度表达研究现状进行归纳和总结，对研究成果进行系统分析与展望。主要分析包括：① 在地理空间数据管理的多尺度数据库与多尺度空间索引方面，多库多版本、一库多版本和一库一版本数据库能够帮助多尺度表达方法构建较好的数据支撑，层次化的多尺度空间索引也是主流的多尺度数据库构建结构。但目前多尺度数据库与多尺度空间索引方法还不具备解决不同层次数据的集成与匹配能力，对不同尺度数据进行实时一致性调整的能力不足；② 在地理空间数据多尺度变换方面，地图自动综合能够较好地与人工智能技术相结合，但由于知识获取的限制，距离实现完全自动综合仍有一段距离；且当前智能化的自动综合研究相关成果主要用于辅助决策，对综合知识的自主学习有待进一步研究；目前多数研究是基于离散的尺度变换模式，对连续尺度变换能力不足；且缺乏强有力的质量控制机制，自动尺度变换结果存在较大的不确定性；③ 在地图多尺度表达方面，地图数据类型多源、种类丰富且使用灵活，多尺度显示的复杂性较高，当前地图可视化对地理信息中隐藏的现象与规律有待进一步挖掘。最后，从智能化自动综合方法、连续多尺度表达模型、深度学习与制图综合及“新”时代多尺度表达等方面对未来发展进行了展望。
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| 关 键 词： | 多尺度表达 自动综合 地理空间数据 尺度变换 人工智能 地图可视化 |
| 收稿时间： | 2022-04-08 |
Review and Prospect: Management,Multi-Scale Transformation and Representation of Geospatial Data |
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| WANG Di,QIAN Haizhong,ZHAO Yuzhe.Review and Prospect: Management,Multi-Scale Transformation and Representation of Geospatial Data[J].Geo-information Science,2022,24(12):2265-2281. |
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| Authors: | WANG Di QIAN Haizhong ZHAO Yuzhe |
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| Institution: | Institute of Geospatial Information, Information Engineering University, Zhengzhou 450000, China |
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| Abstract: | Multi-scale representation is one of the important research contents of geospatial data. This paper summarizes the research status of multi-scale representation of geospatial data from three aspects: geospatial data management, geospatial data scale transformation, and multi-scale representation of the map, and makes a systematic analysis and prospect of current research results. The main conclusions are as follows: ① In terms of multi-scale database and multi-scale spatial index of geospatial data management, three kinds of multi-scale database can provide better data support for multi-scale representation methods, and the hierarchical multi-scale index is the mainstream construction structure for the multi-scale database. However, at present, multi-scale database and multi-scale spatial index still have limited integration and matching ability of data at different levels, and the real-time consistency adjustment ability of data at different scales is also insufficient; ② In terms of the multi-scale transformation of geospatial data, automatic map generalization can be well combined with artificial intelligence technology. But due to the limitation of knowledge acquisition, there is still a long way to achieve automatic map generalization. The relevant achievements of intelligent automatic generalization research are mainly used to assist decision-making now, and the autonomous learning of comprehensive knowledge needs further research. Currently, most of the research is based on a discrete scale transformation model, which is incapable of continuous scale transformation. And due to the lack of a strong quality control mechanism, the results of automatic scaling have great uncertainty; ③ In terms of multi-scale representation of the map, map data types are multi-source, diverse, and flexible to use, and the multi-scale display is highly complex. Currently, the phenomena of hidden geographic information in map visualization need to be further explored. Finally, the future prospect of research on geospatial data presentation is proposed from the aspects of intelligent automatic generalization method, continuous multi-scale representation model, deep learning and cartographic synthesis, and multi-scale representation in the "new" era. |
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| Keywords: | multi-scale representation automatic generalization geospatial data scale transformation artificial intelligence map visualization |
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