面向SDGs和美丽中国评价的地球大数据集成框架与关键技术
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摘要
联合国可持续发展目标(Sustainable Development Goals,SDGs)和美丽中国建设的内涵同根同源,二者都致力于实现国家、区域的社会、环境与经济可持续发展。准确、可靠、及时和分类清晰的数据是实现SDGs和美丽中国精准评价的关键。针对当前可持续发展评价研究中数据单一、时效性差、准确性低及其带来的评价结果不可靠等问题,面向SDGs和美丽中国全景评价,提出了依托网络大数据、遥感大数据与社会经济大数据等地球大数据的集成与标准化框架,分析了网络数据获取与分析、遥感数据地表信息智能提取与处理以及社会经济数据空间化的关键技术,并分别以SDG6水污染事件舆情分析、SDG15森林信息提取、SDGs通用和基础的人口数据空间化为例,研究了地球大数据在可持续发展评价中的技术方案。
Construction of UN Sustainable Development Goals(SDGs)and Beautiful Chinashare the same meaning.Both of them endeavor to achieve national and regional social,environment and economy sustainable development.Accurate,reliable,timely and well classified data is the key for accurate evaluation of sustainable development.In order to address issues such as single data source,poor timeliness,lack of high accuracy and evaluation results unreliable,we puts forward the integration framework and standardization of the bigearth data which includes big network data,big remote sensing data,and big socioeconomic data facing to the evaluation of SDGs and Beautiful China.Then,the key technologies of network data acquisition and analysis,remote sensing data information intelligent extraction and socioeconomic data spatialization are analyzed from different perspectives.Taking the water contamination accident of SDG 6,forest information extraction of SDG 15,population spatialization of common requirements in SDGs as examples,the application of technological routes in supporting sustainable development evaluation based on big earth data are studied consequently.
Construction of UN Sustainable Development Goals(SDGs)and Beautiful Chinashare the same meaning.Both of them endeavor to achieve national and regional social,environment and economy sustainable development.Accurate,reliable,timely and well classified data is the key for accurate evaluation of sustainable development.In order to address issues such as single data source,poor timeliness,lack of high accuracy and evaluation results unreliable,we puts forward the integration framework and standardization of the bigearth data which includes big network data,big remote sensing data,and big socioeconomic data facing to the evaluation of SDGs and Beautiful China.Then,the key technologies of network data acquisition and analysis,remote sensing data information intelligent extraction and socioeconomic data spatialization are analyzed from different perspectives.Taking the water contamination accident of SDG 6,forest information extraction of SDG 15,population spatialization of common requirements in SDGs as examples,the application of technological routes in supporting sustainable development evaluation based on big earth data are studied consequently.
引文
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[2] Chen Jun,Zhang Jun,Zhang Weiwei,et al.Continous Updating and Refinement of Land Cover Data Product[J].Journal of Remote Sensing,2016,20(5):991-1001.[陈军,张俊,张委伟,等,地表覆盖遥感产品更新完善的研究动向[J].遥感学报,2016,20(5):991-1001.]
[3] Gorelick N.Google Earth Engine:Planetary-scale Geospatial Analysis for Everyone[J].Remote Sensing of Environment,2017,202(1):18-27.
[4] Guo H D,Big Earth Data:A New Frontier in Earth and Information Sciences[J].Big Earth Data,2017,1(1-2):4-20.
[5] Guo Huadong.Scientific Big Data-A Footstone of National Strategy for Big Data[J].Bulletin of Chinese Academy of Sciences,2018,33(8):768-773.[郭华东.科学大数据——国家大数据战略的基石[J].中国科学院院刊,2018,33(8):768-773.]
[6] Guo Huadong.Scientific Big Data Drives the Development of Geosciences[J].Science&Technology Review,2018,36(5):1.[郭华东.科学大数据驱动地学学科发展[J].科技导报,2018,36(5):1.]
[7] Guo Huadong.Strategic Priority Research Program(Class A)of the Chinese Academy of Sciences,Earth Big Data Scientific Engineering[J].Bulletin of the Chinese Academy of Sciences,2018,33(8):818-824.[郭华东.中国科学院战略性先导科技专项(A类)地球大数据科学工程[J].中国科学院院刊,2018,33(8):818-824.]
[8] Wei Yanqiang,Li Xin,Gao feng,et al.The United Nations Sustainable Development Goals(SDGs)and the Response Strategies of China[J].Advances in Earth Science,2018,33(10):1083-1093.[魏彦强,李新,高峰,等.联合国2030年可持续发展目标框架及中国应对策略[J].地球科学进展,2018,33(10):1083-1093.]
[9] Wang Yuanzhuo,Jin Xiaolong,Cheng Xueqi.Network Big Data:Present and Future[J].Chinese Journal of Computers,2013,36(6):1125-1138.[王元卓,靳小龙,程学旗.网络大数据:现状与展望[J].计算机学报,2013,36(6):1125-1138.]
[10] Naimi A I,Westreich D J.Big Data:A Revolution That Will Transform How We Live,Work,and Think[J].Mathematics&Computer Education,2014,47(17):181-183.
[11] Yangarber R,Grishman R.NYU:Description of the Proteus/PET System as Used for MUC-7[C]∥Message Understanding Conference,1998:123-131.
[12] Zelenko D,Aone C,Richardella A.Kernel Methods for Relation Extraction[J].Journal of Machine Learning Research,2003,3(3):1083-1106.
[13] Xiong Liyan,Chen Xiaoxia,Zhong Maosheng,et al.Survey on Pairwise of the Learning to Rank[J].Science Technology and Engineering,2017,17(21):184-190.[熊李艳,陈晓霞,钟茂生,等.基于PairWise排序学习算法研究综述[J].科学技术与工程,2017,17(21):184-190.]
[14] Zhang Yanfeng.Learning to Rank Algorithm based on Sparse Representation[D].Xi’an:Xidian University,2014.[张艳凤.基于稀疏表示的排序学习算法[D].西安:西安电子科技大学,2014.]
[15] Zhou Junyu,Dai Yueming,Wu Dinghui.Factorisation Recommendation Algorithm based on Pairwise Ranking Learning[J].Computer Applications and Software,2016,33(6):255-259.[周俊宇,戴月明,吴定会.基于Pairwise排序学习的因子分解推荐算法[J].计算机应用与软件,2016,33(6):255-259.]
[16] Negahban S,Oh S,Shah D.Iterative Ranking from Pair-wise Comparisons[J].Advances in Neural Information Processing Systems,2012,3(93):2483-2491.
[17] Li Xianhui,Yu Zhengtao,Wei Sichao,et al.Deeping Learning Expert Ranking Method based on Listwise[J].Pattern Recognition and Artificial Intelligence,2015,28(11):976-982.[李贤慧,余正涛,魏斯超,等.基于Listwise的深度学习专家排序方法[J].模式识别与人工智能,2015,28(11):976-982.]
[18] Zhu Jianzhang,Shi Qiang,Chen Fenge,et al.Research Status and Development Trends of Remote Sensing Big Data[J].Journal of Image and Graphies,2016,21(11):1425-1439.[朱建章,石强,陈凤娥,等.遥感大数据研究现状与发展趋势[J].中国图象图形学报,2016,21(11):1425-1439.]
[19] Guo Y,Feng N,Christopher S A,et al.Satellite Remote Sensing of Fine Particulate Matter(PM 2.5)Air Quality over Beijing Using MODIS[J].International Journal of Remote Sensing,2014,35(17):6522-6544.
[20] Cao X,Chen J,Imura H,et al.A SVM-based Method to Extract Urban Areas from DMSP-OLS and SPOT VGT Data[J].Remote Sensing of Environment,2009,113(10):2205-2209.
[21] Shi K,Chen Y,Yu B,et al.Modeling Spatio-temporal CO2,(Carbon Dioxide)Emission Dynamics in China from DMSPOLS Nighttime Stable Light Data Using Panel Data Analysis[J].Applied Energy,2016,168:523-533.
[22] Zhuo L,Zheng J,Zheng J,et al.Modelling the Population Density of China at the Pixel Level based on DMSP/OLS Non-radiance-calibrated Night-time Light Images[J].International Journal of Remote Sensing,2009,30(4):1003-1018.
[23] Wang Anzhou,Zhang Guibin,Geng Xiuli.Research on Urban Landscape Dynamics of Zhengzhou City during 1988-2002[J].Research of Soil and Water Conservation,2010,17(2):190-194.[王安周,张桂宾,耿秀丽.1988~2002年郑州市景观格局演变分析[J].水土保持研究,2010,17(2):190-194.]
[24] Baumann M,Ozdogan M,Kuemmerle T,et al.Using the Landsat Record to Detect Forest-cover Changes during and After the Collapse of the Soviet Union in the Temperate Zone of European Russia[J].Remote Sensing of Environment,2012,124(124):174-184.
[25] Doustfatemeh I,Baleghi Y.Comprehensive Urban Area Extraction from Multi-spectral Medium Spatial Resolution Remote-sensing Imagery based on a Novel Structural Feature[J].International Journal of Remote Sensing,2016,37(18):4225-4242.
[26] Wang M,Zhang S Q.Road Extraction from High-spatial-resolution Remotely Sensed Imagery by Combining Multi-profile Analysis and Extended Snakes Model[J].International Journal of Remote Sensing,2011,32(21):6349-6365.
[27] Ma Y,Wu H,Wang L,et al.Remote Sensing Big Data Computing:Challenges and Opportunities[J].Future Generation Computer Systems,2015,51:47-60.
[28] Song Weijing,Liu Peng,Wang Lizhe,et al.Intelligent Processing of Remote Sensing Big Data:Status and Challenges[J].Journal of Engineering Studies,2014,6(3):259-265.[宋维静,刘鹏,王力哲,等.遥感大数据的智能处理:现状与挑战[J].工程研究—跨学科视野中的工程,2014,6(3):259-265.]
[29] Hinton G E,Osindero S,Teh Y W.A Fast Learning Algorithm for Deep Belief Nets[J].Neural Computation,2006,18(7):1527.
[30] Fu Weifeng,Zou Weibao.Review of Remote Sensing Image Classification based on Deep Learning[J].Application Research of Computers,2018,(12):1-6.[付伟锋,邹维宝.深度学习在遥感影像分类中的研究进展[J].计算机应用研究,2018,(12):1-6.]
[31] He K,Zhang X,Ren S,et al.Deep Residual Learning for Image Recognition[C]∥IEEE International Conference on Computer Vision and Pattern Recognition(CVPR),2016.
[32] Redmon J,Divvala S,Girshick R,et al.You Only Look Once:Unified,Real-time Object Detection[C]∥IEEE International Conference on Computer Vision and Pattern Recognition,2016.
[33] Long J,Shelhamer E,Darrell T.Fully Convolutional Networks for Semantic Segmentation[C]∥IEEE International Conference on Computer Vision and Pattern Recognition,2015.
[34] Ren Chong.Forest Types Precise Classification and Forest Resources Change Monitoring based on Medium and High Spatial Resolution Remote Sensing Images[D].Beijing:Chinese Academy of Forest,2016.[任冲.中高分辨率遥感影像森林类型精细分类与森林资源变化监测技术研究[D].北京:中国林业科学研究院,2016.]
[35] Chen Jun,Ren Huiru,Geng Wen,et al.Quantitative Measurement and Monitoring Sustainable Development Goals(SDGs)with Geospatial Information[J].Geomatics World,2018,25(1):1-7.[陈军,任惠茹,耿雯,等.基于地理信息的可持续发展目标(SDGs)量化评估[J].地理信息世界,2018,25(1):1-7.]
[36] Li Fei,Zhang Shuwen,Yang Jiuchun,et al.A Review on Research about Spatialization of Socio-economic Data[J].Geography and Information Science,2014,30(4):102-107.[李飞,张树文,杨久春,等.社会经济数据空间化研究进展[J].地理与地理信息科学,2014,30(4):102-107.]
[37] Dong Nan,Yang Xiaohuan,Cai Hongyan.Research Progress and Perspective on the Spatialization of Population Data[J].Journal of Geography Information Science,2016,18(10):1295-1304.[董南,杨小唤,蔡红艳.人口数据空间化研究进展[J].地球信息科学,2016,18(10):1295-1304.]
[38] Ye Jing,Yang Xiaohuan,Jiang Dong.The Grid Scale Effect Analysis on Town Leveled Population Statistical Data Spatialization[J].Journal of Geography Information Science,2010,12(1):40-47.[叶靖,杨小唤,江东.乡镇级人口统计数据空间化的格网尺度效应分析——以义乌市为例[J].地球信息科学学报,2010,12(1):40-47.]
[39] Chen Zhentuo.Study on the Grid Transformation of Population Data at the Service of Earthquake Emergency:A Case Study in Yunnan Province[D].Beijing:Institute of Geology,China Earthquake Administration,2012.[陈振拓.服务于地震应急的人口数据格网化方法研究——以云南省为例[D].北京:中国地震局地质研究所,2012.]
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