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

ZHU Jianjun; SONG Yingchun; HU Jun; ZOU Bin; WU Lixin

doi:10.13203/j.whugis20210232

Volume 46 Issue 7

Jul. 2021

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Geomatics and Information Science of Wuhan University > 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

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

1.
School of Geoscience and Info-Physics, Central South University, Changsha 410083, China

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Graduate Education and Teaching Reform in Hunan Province 2020JGZD014

the Research Project of Teaching Reform in Colleges and Universities of Hunan Province HNJG-2020-0034

the Key Project of Graduate Education and Teaching Reform in Central South University 2020JGA011

the Key Project of Education and Teaching Reform in Central South University 2020JY001

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Author Bio:
ZHU Jianjun, PhD, professor, specializes in error data processing and its application in InSAR.E-mail: zjj@csu.edu.cn
Corresponding author:
SONG Yingchun, PhD, professor. E-mail: csusyc@mail.csu.edu.cn
Received Date: May 10, 2021
Published Date: July 09, 2021

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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.
- intelligent surveying and mapping,
- big data,
- artificial intelligence,
- data processing theory,
- challenges,
- development

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[1]	刘经南, 郭文飞, 郭迟, 等. 智能时代泛在测绘的再思考[J]. 测绘学报, 2020, 49(4): 403-414 https://www.cnki.com.cn/Article/CJFDTOTAL-CHXB202004001.htm Liu Jingnan, Guo Wenfei, Guo Chi, et al. Rethinking Ubiquitous Mapping in the Intelligentage[J]. Acta Geodaetica et Cartographica Sinica, 2020, 49(4): 403-414 https://www.cnki.com.cn/Article/CJFDTOTAL-CHXB202004001.htm
[2]	龚健雅. 人工智能时代测绘遥感技术的发展机遇与挑战[J]. 武汉大学学报·信息科学版, 2018, 43(2): 1 788-1 796 http://ch.whu.edu.cn/article/id/6262 Gong Jianya. Chances and Challenges for Development of Surveying and Remote Sensing in the Age of Artificial Intelligenc[J]. Geomatics and Information Science of Wuhan University, 2018, 43(2): 1 788-1 796 http://ch.whu.edu.cn/article/id/6262
[3]	李德仁, 姚远, 邵振峰. 智慧城市中的大数据[J]. 武汉大学学报·信息科学版, 2014, 39(6): 631-640 http://ch.whu.edu.cn/article/id/2999 Li Deren, Yao Yuan, Shao Zhenfeng. Big Data in Smart City[J]. Geomatics and Information Science of Wuhan University, 2014, 39(6): 631-640 http://ch.whu.edu.cn/article/id/2999
[4]	宁津生. 测绘科学与技术转型升级发展战略研究[J]. 武汉大学报·信息科学版, 2019, 44(1): 1-9 https://www.cnki.com.cn/Article/CJFDTOTAL-WHCH201901001.htm Ning Jinsheng. Research on the Development Strategy of Surveying and Mapping Science and Technology Transformation and Upgrading[J]. Geomatics and Information Science of Wuhan University, 2019, 44(1): 1-9 https://www.cnki.com.cn/Article/CJFDTOTAL-WHCH201901001.htm
[5]	刘经南. 大数据与位置服务[J]. 测绘科学, 2014, 39(3): 3-9 https://www.cnki.com.cn/Article/CJFDTOTAL-CHKD201403002.htm Liu Jingnan. Big Data and Location Services[J]. Science of Surveying and Mapping, 2014, 39(3): 3-9 https://www.cnki.com.cn/Article/CJFDTOTAL-CHKD201403002.htm
[6]	郭雷. 不确定性动态系统的估计、控制与博弈[J]. 中国科学: 信息科学, 2020, 50(9): 1 327-1 344 https://www.cnki.com.cn/Article/CJFDTOTAL-PZKX202009004.htm Guo Lei. Estimation, Control, and Games of Dynamical Systems with Uncertainty[J]. Scientia Sinica: Informationis, 2020, 50(9): 1 327-1 344 https://www.cnki.com.cn/Article/CJFDTOTAL-PZKX202009004.htm
[7]	靳小龙, 王元卓, 程学旗. 大数据的研究体系与现状[J]. 信息通信技术, 2013, 7(6): 35-43 https://www.cnki.com.cn/Article/CJFDTOTAL-OXXT201306008.htm Jin Xiaolong, Wang Yuanzhuo, Cheng Xueqi. Research System and Status of Big Data[J]. Information and Communications Technologies, 2013, 7(6): 35-43 https://www.cnki.com.cn/Article/CJFDTOTAL-OXXT201306008.htm
[8]	李德仁. 展望大数据时代的地球空间信息学[J]. 测绘学报, 2016, 45(4): 379-384 https://www.cnki.com.cn/Article/CJFDTOTAL-CHXB201604002.htm Li Deren. Towards Geo-Spatial Information Science in Big Data Era[J]. Acta Geodaetica et Cartographica Sinica, 2016, 45(4): 379-384 https://www.cnki.com.cn/Article/CJFDTOTAL-CHXB201604002.htm
[9]	Palpanas T, Vlachos M, Keogh E, et al. Onlineamnesic Approximation of Streaming Time Series[C]// IEEE International Conference on Data Engineering, Boston, MA, USA, 2004
[10]	郑志明, 吕金虎, 韦卫, 等. 精准智能理论: 面向复杂动态对象的人工智能[J]. 中国科学: 信息科学, 2021, 51(4): 678-690 Zheng Zhiming, Lü Jinhu, Wei Wei, et al. Refined Intelligence Theory: Artificial Intelligence Towards Complex Dynamic Objects[J]. Scientia Sinica: Informationis, 2021, 51(4): 678-690
[11]	吴华意, 黄蕊, 游兰, 等. 出租车轨迹数据挖掘进展[J]. 测绘学报, 2019, 48(11) : 1 341-1 356 https://www.cnki.com.cn/Article/CJFDTOTAL-CHXB201911002.htm Wu Huayi, Huang Rui, You Lan, et al. Recent Progress in Taxi Trajectory Data Mining[J]. Acta Geodaetica et Cartographica Sinica, 2019, 48(11): 1 341-1 356 https://www.cnki.com.cn/Article/CJFDTOTAL-CHXB201911002.htm
[12]	Koyama C N, Watanabe M. Assessing the Impact of Precipitation on L-band SAR Forest Observation: An ALOS-2 Big Data Case Study in the Tropics[C]// The 13th European Conference on Synthetic Aperture Radar (EUSAR 2021), Online Conference, 2021
[13]	黄露. 基于机器学习的汶川震区滑坡灾害气象预警模型研究[D]. 武汉: 中国地质大学, 2016 Huang Lu. Research on Meteorological Early-Warning Model of Landslides in Wenchuan Earthquake Area Based on Machine Learning[D]. Wuhan: China University of Geosciences, 2016
[14]	沈焕锋, 刘露, 岳林蔚, 等. 多源DEM融合的高差拟合神经网络方法[J]. 测绘学报, 2018, 47(6): 854-863 https://www.cnki.com.cn/Article/CJFDTOTAL-CHXB201806019.htm Shen Huanfeng, Liu Lu, Yue Linwei, et al. A Multi-source DEM Fusion Method Based on Elevation Difference Fitting Neural Network[J]. Acta Geodaetica et Cartographica Sinica, 2018, 47(6): 854-863 https://www.cnki.com.cn/Article/CJFDTOTAL-CHXB201806019.htm
[15]	杨必胜, 宗泽亮, 陈驰, 等. 车载探地雷达地下目标实时探测法[J]. 测绘学报, 2020, 49(7) : 874-883 https://www.cnki.com.cn/Article/CJFDTOTAL-CHXB202007009.htm Yang Bisheng, Zong Zeliang, Chen Chi, et al. Real Time Approach for Underground Objects Detection from Vehicle-Borne Ground Penetrating Radar[J]. Acta Geodaetica et Cartographica Sinica, 2020, 49(7): 874-883 https://www.cnki.com.cn/Article/CJFDTOTAL-CHXB202007009.htm
[16]	Qiu C, Tong X, Schmitt M, et al. Multi-level Feature Fusion-based CNN for Local Climate Zone Classification from Sentinel-2 Images: Benchmark Results on the So2Sat LCZ42 Dataset[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020, 13: 2 793-2 806 doi: 10.1109/JSTARS.2020.2995711
[17]	Zhu X X, Tuia D, Mou L, et al. Deep Learning in Remote Sensing: A Review[J]. IEEE Geoscience and Remote Sensing Magazine, 2017, DOI: 10.1109/MGRS.2017.2762307
[18]	赵传, 郭海涛, 卢俊, 等. 基于深度残差网络的机载LiDAR点云分类[J]. 测绘学报, 2020, 49(2) : 202-213 https://www.cnki.com.cn/Article/CJFDTOTAL-CHXB202002008.htm Zhao Chuan, Guo Haitao, Lu Jun, et al. Airborne LiDAR Point Cloud Classification Based on Deep Residual Network[J]. Acta Geodaetica et Cartographica Sinica, 2020, 49(2): 202-213 https://www.cnki.com.cn/Article/CJFDTOTAL-CHXB202002008.htm
[19]	Zhu Xiaoxiang, Montazeri S, Ali M, et al. Deep Learning Meets SAR: Concepts, Models, Pitfalls, and Perspectives[J]. IEEE Geoscience and Remote Sensing Magazine, 2021, DOI: 10.1109/MGRS.2020.3046356
[20]	Zhang L, Dong H, Zou B. Efficiently Utilizing Complex-Valued PolSAR Image Data via a Multi-task Deep Learning Framework[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2019, 157: 59-72 doi: 10.1016/j.isprsjprs.2019.09.002
[21]	Brigot G, Simard M, Colin-Koeniguer E, et al. Retrieval of Forest Vertical Structure from PolInSAR Data by Machine Learning Using LiDAR-Derived Features[J]. Remote Sensing, 2019, 11(4): 381 doi: 10.3390/rs11040381
[22]	张兵. 遥感大数据时代与智能信息提取[J]. 武汉大学学报·信息科学版, 2018, 43(12): 108-118 http://ch.whu.edu.cn/article/id/6270 Zhang Bing. Remotely Sensed Big Data Era and Intelligent Information Extraction[J]. Geomatics and Information Science of Wuhan University, 2018, 43(12): 108-118 http://ch.whu.edu.cn/article/id/6270
[23]	汤寓麟, 金绍华, 边刚, 等. 侧扫声呐识别沉船影像的迁移学习卷积神经网络法[J]. 测绘学报, 2021, 50(2) : 260-269 Tang Yulin, Jin Shaohua, Bian Gang, et al. The Transfer Learning with Convolutional Neural Network Method of Side-Scan Sonar to Identify Wreck Images[J]. Acta Geodaetica et Cartographica Sinica, 2021, 50(2): 260-269
[24]	段佩祥, 钱海忠, 何海威, 等. 案例支撑下的朴素贝叶斯树状河系自动分级方法[J]. 测绘学报, 2019, 48(8) : 975-984 https://www.cnki.com.cn/Article/CJFDTOTAL-CHXB201908005.htm Duan Peixiang, Qian Haizhong, He Haiwei, et al. Naïve Bayes-based Automatic Classification Method of Tree-Like River Networks Upported by Cases[J]. Acta Geodaetica et Cartographica Sinica, 2019, 48(8): 975-984 https://www.cnki.com.cn/Article/CJFDTOTAL-CHXB201908005.htm
[25]	刘青豪, 张永红, 邓敏, 等. 大范围地表沉降时序深度学习预测法[J]. 测绘学报, 2021, 50(3) : 396-404 Liu Qinghao, Zhang Yonghong, Deng Min, et al. Time Series Prediction Method of Large-Scale Surface Subsidence Based on Deep Learning[J]. Acta Geodaetica et Cartographica Sinica, 2021, 50(3): 396-404
[26]	王鹤, 曾永年. 城市扩展极限学习机模型[J]. 测绘学报, 2018, 47(12): 1 680-1 690 doi: 10.11947/j.AGCS.2018.20170586 Wang He, Zeng Yongnian. Urban Expansion Model Based on Extreme Learning Machine[J]. Acta Geodaetica et Cartographica Sinica, 2018, 47(12): 1 680-1 690 doi: 10.11947/j.AGCS.2018.20170586
[27]	赵鹏大. 大数据时代呼唤各科学领域的数据科学[J]. 中国科技奖励, 2014(183): 29-30 https://www.cnki.com.cn/Article/CJFDTOTAL-ZKJL201409014.htm Zhao Pengda. Big Data Era Calls for Data Science in Various Fields of Science[J]. Chin Sci Tech Award, 2014(183): 29-30 https://www.cnki.com.cn/Article/CJFDTOTAL-ZKJL201409014.htm

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

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