朱建军, 宋迎春, 胡俊, 邹滨, 吴立新. 测绘大数据时代数据处理理论面临的挑战与发展[J]. 武汉大学学报 ( 信息科学版), 2021, 46(7): 1025-1031. DOI: 10.13203/j.whugis20210232
引用本文: 朱建军, 宋迎春, 胡俊, 邹滨, 吴立新. 测绘大数据时代数据处理理论面临的挑战与发展[J]. 武汉大学学报 ( 信息科学版), 2021, 46(7): 1025-1031. DOI: 10.13203/j.whugis20210232
ZHU Jianjun, SONG Yingchun, HU Jun, ZOU Bin, WU Lixin. Challenges and Development of Data Processing Theory in the Era of Surveying and Mapping Big Data[J]. Geomatics and Information Science of Wuhan University, 2021, 46(7): 1025-1031. DOI: 10.13203/j.whugis20210232
Citation: ZHU Jianjun, SONG Yingchun, HU Jun, ZOU Bin, WU Lixin. Challenges and Development of Data Processing Theory in the Era of Surveying and Mapping Big Data[J]. Geomatics and Information Science of Wuhan University, 2021, 46(7): 1025-1031. DOI: 10.13203/j.whugis20210232

测绘大数据时代数据处理理论面临的挑战与发展

Challenges and Development of Data Processing Theory in the Era of Surveying and Mapping Big Data

  • 摘要: 随着信息技术的发展、测绘大数据和人工智能的兴起,数据缺乏不再是一个问题。可是,现有的测绘数据处理技术一直追求数据的准确性(微观),而大数据研究则恰恰允许数据的混杂性、不确定性(宏观)。因此,尽管传统测绘数据处理理论在微观数据处理方面积累了大量的技术优势,而大数据的规模性和复杂性使得传统的计算模型和分析算法无法有效地支撑大数据的高效分析处理。作为开启智能时代“大门钥匙”的数据处理理论与方法,如何适应新技术的挑战与机遇是值得深入思考的问题。在大数据驱动下,大规模的数据挖掘、机器学习和深度学习等新思想和新方法正在蓬勃发展,极大地促进了场景内外多源异质大数据的融合,从而有效地从多种传感器数据中提取地表特征信息,不断提升测绘信息获取和分析能力。因此,测绘数据处理理论也需要同步跟进,现有的数据处理方法也需要进行智能化。结合智能测绘的前沿热点、发展趋势和存在的挑战,探索数据处理理论扩展的方向,一是希望能够推动测绘数据处理理论的进一步发展,二是希望为有兴趣研究测绘大数据领域的研究生提供学习参考。

     

    Abstract: With the development of information technology, the rise of surveying and mapping big data and artificial intelligence, the lack of data is no longer a problem. However, the existing surveying and mapping data processing technology has been pursuing the accuracy of data (micro), and big data research just allows the data to be mixed and uncertain (macro). Therefore, although the traditional surveying and mapping data processing theory has accumulated a large number of technical advantages in micro data processing, the large-scale and complexity of big data has become increasingly prominent, in which traditional calculation model and analysis algorithm cannot effectively support the efficient analysis and processing of big data. As the key to the intelligent era, data processing theory and method, how to adapt to the challenges and opportunities of new technology is worth our deep thinking. Driven by big data, new ideas and methods such as large-scale data mining, machine learning and deep learning are booming, which greatly promote the fusion of multi-source heterogeneous big data inside and outside the scene, effectively extract surface feature information from a variety of sensor data, and constantly improve the ability of surveying and mapping information acquisition and analysis. We think that the theory of surveying and mapping data also needs to be followed up, and the existing data processing methods need to be intelligent. Combined with the frontier hot spots, development trends and existing challenges of intelligent surveying and mapping, this paper explores the expansion direction of data processing theory. One is to promote the further development of surveying data processing theory, and the other is to provide reference for graduate students who are interested in entering the field of surveying and mapping big data.

     

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