人工智能GIS软件技术体系初探

doi:10.12082/dqxxkx.2020.190701

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

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. 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 Guanfu^1,^2,^3,^*(), LU Hao^1,², WANG Chenliang³, HU Chenpu^1,², HUANG Kejia^1,²

^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/Abstract

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

宋关福, 卢浩, 王晨亮, 胡辰璞, 黄科佳. 人工智能GIS软件技术体系初探[J]. 地球信息科学学报, 2020, 22(1): 76-87.DOI:10.12082/dqxxkx.2020.190701

SONG Guanfu, LU Hao, WANG Chenliang, HU Chenpu, HUANG Kejia. A Tentative Study on System of Software Technology for Artificial Intelligence GIS[J]. Journal of Geo-information Science, 2020, 22(1): 76-87.DOI:10.12082/dqxxkx.2020.190701

图/表 16

图1

图2

图3

图4

图5

图6

图7

表1

图8

图9

图10

图11

图12

图13

图14

图15

参考文献 63

[1]	Karpatne A Ebert-uphoff I Ravela S , et al. Machine learning for the geosciences: Challenges and opportunities[J]. IEEE Transactions on Knowledge and Data Engineering, 2019,31(8):1544-1554.
[2]	Zhu X X, Tuia D, Mou L , et al. Deep learning in remote sensing: A comprehensive review and list of resources[J]. IEEE Geoscience and Remote Sensing Magazine, 2017,5(4):8-36.
[3]	Zhang L P, Zhang L F, Du B . Deep learning for remote sensing data: A technical tutorial on the state of the art[J]. IEEE Geoscience and Remote Sensing Magazine, 2016,4(2):22-40.
[4]	Shen C . A transdisciplinary review of deep learning research and its relevance for water resources scientists[J]. Water Resources Research, 2018,54(11):8558-8593.
[5]	Vopham T, Hart J E, Laden F , et al. Emerging trends in geospatial artificial intelligence (geoAI): Potential applications for environmental epidemiology[J]. Environmental Health, 2018,17(1). DOI: 10.1186/s12940-018-0386-x.
[6]	Kamel Boulos M N, Peng G, Vopham T . An overview of GeoAI applications in health and healthcare[J]. International Journal of Health Geographics, 2019,18(1). DOI: 10.1186/s12942-019-0171-2.
[7]	Helbich M, Yao Y, Liu Y , et al. Using deep learning to examine street view green and blue spaces and their associations with geriatric depression in Beijing, China[J]. Environment International, 2019,126(1):107-117.
[8]	Cresson R . A framework for remote sensing images processing using deep learning techniques[J]. IEEE Geoscience and Remote Sensing Letters, 2019,16(1):25-29.
[9]	Yan X F, Ai T H, Yang M , et al. A graph convolutional neural network for classification of building patterns using spatial vector data[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2019,150(1):259-273.
[10]	Comber S, Arribas-Bel D . Machine learning innovations in address matching: A practical comparison of word2vec and CRFs[J]. Transactions in GIS, 2019,23(2):334-348.
[11]	Fan J, Li Q, Hou J , et al. A spatiotemporal prediction framework for air pollution based on deep RNN[J]. Remote Sensing and Spatial Information Sciences, 2017, IV-4/W2(1):15-22.
[12]	Bai Y, Zeng B, Li C , et al. An ensemble long short-term memory neural network for hourly PM2.5 concentration forecasting[J]. Chemosphere, 2019,222(1):286-294.
[13]	Goodchild M F . Towards geodesign: Repurposing cartography and GIS?[J]. Cartographic Perspectives, 2010,66(2010):7-22.
[14]	钟耳顺 . 地理控制与实况地理学——关于GIS发展的思考[J]. 地球信息科学学报, 2013,15(6):783-792.
	[ Zhong E S . Geo-control and live geography: Some thoughts on the Direction of GIS[J]. Journal of Geo-information Science, 2013,15(6):783-792. ]
[15]	王劲峰, 葛咏, 李连发 , 等. 地理学时空数据分析方法[J]. 地理学报, 2014,69(9):1326-1345.
	[ Wang J F, Ge Y, Li L F , et al. Spatiotemporal data analysis in geography[J]. Acta Geographica Sinica, 2014,69(9):1326-1345. ]
[16]	王劲峰, 廖一兰, 刘鑫 . 空间数据分析教程(第二版)[M]. 北京: 科学出版社, 2019.
	[ Wang J F, Liao Y L, Liu X. Tutorial of spatial data analysis (second edtion)[M]. Beijing: Science Press, 2019. ]
[17]	Lary D J, Alavi A H, Gandomi A H , et al. Machine learning in geosciences and remote sensing[J]. Geoscience Frontiers, 2016,7(1):3-10.
[18]	岳天祥 . 地球表层建模研究进展[J]. 遥感学报, 2011,15(6):1111-1130.
	[ Yue T X . Progress in earth surface modeling. Journal of Remote Sensing, 2011,15(6):1111-1130. ]
[19]	Lecun Y, Bengio Y, Hinton G . Deep learning[J]. Nature, 2015,521(7553):436-444.
[20]	Li P, Xie J, Wang Q , et al. Is second-order information helpful for large-scale visual recognition?[C]. Proceedings of the IEEE International Conference on Computer Vision, 2017: 2089-2097.
[21]	Lin T-Y, Dollar P, Girshick R , et al. Feature pyramid Networks for object detection[C]. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017: 936-944.
[22]	Huang J, Rathod V, Sun C , et al. Speed/Accuracy trade-offs for modern convolutional object detectors[C]. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017: 3296-3297.
[23]	Chen G, Choi W, Yu X , et al. Learning efficient object detection models with knowledge distillation[C]. Advances in Neural Information Processing Systems, 2017: 743-752.
[24]	Zhang Z, Peng H . Deeper and wider siamese networks for real-time visual tracking[C]. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019.
[25]	Bertinetto L, Valmadre J, Henriques J F , et al. Fully-convolutional siamese networks for object tracking[C]. European conference on computer vision, 2016: 850-865.
[26]	Zhou B, Zhao H, Puig X , et al. Semantic understanding of scenes through the ADE20K dataset[J]. International Journal of Computer Vision, 2019,127(3):302-321.
[27]	呼延康, 樊鑫, 余乐天 , 等. 图神经网络回归的人脸超分辨率重建[J]. 软件学报, 2018,29(4):914-925.
	[ Hu Y K, Fan X, Yu L T, Luo Z X , Graph based neural network regression strategy for facial image super-resolution[J]. Journal of Software, 2018, 29(4):914-925. ].
[28]	Zhang Y, Li K, Li K , et al. Image super-resolution using very deep residual channel attention networks[C]. Proceedings of the European Conference on Computer Vision (ECCV), 2018: 286-301.
[29]	Dai T, Cai J, Zhang Y , et al. Second-order attention network for single image super-resolution[C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2019: 11065-11074.
[30]	Reichstein M, Camps-Valls G, Stevens B , et al. Deep learning and process understanding for data-driven Earth system science[J]. Nature, 2019,566(7743):195-204.
[31]	Li S, Song W, Fang L , et al. Deep learning for hyperspectral image classification: An overview[J]. IEEE Transactions on Geoscience and Remote Sensing, 2019,57(9):6690-6709.
[32]	Zhang S, Wu G, Costeira J P , et al. FCN-rLSTM: Deep spatio-temporal neural networks for vehicle counting in city cameras[C]. 2017 IEEE International Conference on Computer Vision (ICCV), 2017: 3687-3696.
[33]	Ren H, Cromwell E, Kravitz B , et al. Using deep learning to fill spatio-temporal data gaps in hydrological monitoring networks[J]. Hydrology and Earth System Sciences Discussions, 2019.DOI: 10.5194/hess-2019-196.
[34]	Li X, Peng L, Hu Y , et al. Deep learning architecture for air quality predictions[J]. Environmental Science and Pollution Research, 2016,23(22):22408-22417.
[35]	Apte J S, Messier K P, Gani S , et al. High-resolution air pollution mapping with google street view cars: Exploiting big data[J]. Environmental Science and Technology, 2017,51(12):6999-7008.
[36]	Qi Y, Li Q, Karimian H , et al. A hybrid model for spatiotemporal forecasting of PM2.5 based on graph convolutional neural network and long short-term memory[J]. Science of the Total Environment, 2019,664(1):1-10.
[37]	Wang J, Song G . A deep spatial-temporal ensemble model for air quality prediction[J]. Neurocomputing, 2018,314(1):198-206.
[38]	Zhou Y, Chang F J, Chang L C , et al. Explore a deep learning multi-output neural network for regional multi-step-ahead air quality forecasts[J]. Journal of Cleaner Production, 2019,209(1):134-145.
[39]	Huang C, Zhang C, Zhao J , et al. MiST: A multiview and multimodal spatial-temporal learning framework for citywide abnormal event forecasting[C]. The World Wide Web Conference on WWW '19, 2019: 717-728.
[40]	Zhang J B, Zheng Y, Qi D K , et al. Predicting citywide crowd flows using deep spatio-temporal residual networks[J]. Artificial Intelligence, 2018,259(1):147-166.
[41]	Li Y G, Yu R, Shahabi C , et al. Diffusion convolutional recurrent neural network: Data-driven traffic forecasting[C]. International conference on learning representations (ICLR '18), 2018: 1-16.
[42]	Rakhlin A, Davydow A, Nikolenko S . Land cover classification from satellite imagery with U-Net and lovász-Softmax loss. 2018 IEEE/CVF conference on computer vision and pattern recognition workshops (CVPRW): IEEE, 2018: 257-2574.
[43]	Kuo T S, Tseng K S, Yan J W , et al. Deep aggregation Net for land cover classification. The IEEE conference on computer vision and pPattern recognition (CVPR), 2018: 252-256.
[44]	Davydow A, Nikolenko S . Land cover classification with superpixels and jaccard index post-optimization[C]. 2018 IEEE/CVF conference on computer vision and pattern recognition workshops (CVPRW), 2018: 280-2804.
[45]	Ma L, Li M, Ma X , et al. A review of supervised object-based land-cover image classification[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2017,130(1):277-293.
[46]	Zhou L, Zhang C, Wu M . D-LinkNet: LinkNet with pretrained encoder and dilated convolution for high resolution satellite imagery road extraction[C]. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2018: 1922-1924.
[47]	Ghosh A, Ehrlich M, Shah S , et al. Stacked U-Nets for ground material segmentation in remote sensing imagery[C]. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2018: 252-2524.
[48]	Olorisade B K, Brereton P, Andras P . Reproducibility in machine learning-based studies: An example from text mining[C]. ICML Reproducibility in ML Workshop, 2017: 471-486.
[49]	Hutson M . Artificial intelligence faces reproducibility crisis[J]. Science, 2018,359(6377):725-726.
[50]	周成虎 . 全空间地理信息系统展望[J]. 地理科学进展, 2015,34(2):129-131.
	[ Zhou C H . Prospects on pan-spatial information system[J]. Progress in geography, 2015,34(2):129-131. ]
[51]	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(12):1788-1788.
	[ Gong J Y . Chances and challenges for development of surveying and remote sensing in the age of artificial intelligence[J]. Geomatics and Information Science of Wuhan University, 2018,43(12):1788-1796. ]
[52]	Qayyum U, Ahsan Q, Mahmood Z . IMU aided RGB-D SLAM[C]. 2017 14 ^th International bhurban conference on applied sciences and technology (IBCAST) , 2017: 337-341.
[53]	Vosselman G . Design of an indoor mapping system using three 2D laser scanners and 6 DOF SLAM[J]. ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, 2014, II-3(9):173-179.
[54]	Younes G, Asmar D, Shammas , et al. Keyframe-based monocular SLAM: Design, survey, and future directions[J]. Robotics and Autonomous Systems, 2017,98(1):67-88.
[55]	Huang G . Visual-Inertial Navigation: A concise review[C]. 2019 International Conference on Robotics and Automation (ICRA), 2019: 9572-9582.
[56]	Tombari F, Salti S, Di stefano L . Performance evaluation of 3D keypoint detectors[J]. International Journal of Computer Vision, 2013,102(1):198-220.
[57]	孔云峰 . 地理视频数据模型设计及网络视频GIS实现[J]. 武汉大学学报: 信息科学版, 2010,35(2):133-137.
	[ Kong Y F . Design of Geovideo data model and implementation of web-based videoGIS[J]. Geomatics and Information Science of Wuhan University, 2010,35(2):133-137. ]
[58]	张兴国, 刘学军, 王思宁 , 等. 监控视频与2D地理空间数据互映射[J]. 武汉大学学报(信息科学版), 2015,40(8):1130-1136.
	[ Zhang X G, Liu X J, Wang S N, Liu Y . Mutual mapping between surveillance video and 2D geospatial data[J]. Geomatics and Information Science of Wuhan University, 2015,40(8):1130-1136. ]
[59]	Lewis P, Fotheringham S, Winstanley A . Spatial video and GIS[J]. International Journal of Geographical Information Science, 2011,25(5):697-716.
[60]	Milosavljević A, Dimitrijević A, Rančić D . GIS-augmented video surveillance[J]. International Journal of Geographical Information Science, 2010,24(9):1415-1433.
[61]	Zador A M . A critique of pure learning and what artificial neural networks can learn from animal brains[J]. Nature Communications, 2019,10(1):3770.
[62]	Banino A, Barry C, Uria B , et al. Vector-based navigation using grid-like representations in artificial agents[J]. Nature, 2018,557(7705):429-433.
[63]	Pei J, Deng L, Song S , et al. Towards artificial general intelligence with hybrid Tianjic chip architecture[J]. Nature, 2019,572(7767):106-111.

软件类别	软件模块	数据准备	模型构建	模型应用
服务器GIS	SuperMap iServer 机器学习服务			√
服务器GIS	SuperMap iServer数据科学服务	√	√	√
桌面GIS	SuperMap iDesktopX	√	√	√
组件式GIS	SuperMap iObjects Python	√	√	√

人工智能GIS软件技术体系初探

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

RichHTML

PDF (PC)

赞

可视化

被引次数

摘要/Abstract

引用本文

使用本文

图/表 16

参考文献 63

相关文章 4

Metrics

本文评价

推荐阅读 0

[1]	宋关福, 陈勇, 罗强, 武梦瑶. GIS基础软件技术体系发展及展望[J]. 地球信息科学学报, 2021, 23(1): 2-15.
[2]	苏奋振, 吴文周, 张宇, 康路, 李晓恩. 从地理信息系统到智能地理系统[J]. 地球信息科学学报, 2020, 22(1): 2-10.
[3]	张丽英, 裴韬, 陈宜金, 宋辞, 刘小茜. 基于街景图像的城市环境评价研究综述[J]. 地球信息科学学报, 2019, 21(1): 46-58.
[4]	杜云艳, 周成虎, 邵全琴, 苏奋振, 史忠植, 叶施仁. 案例推理的地学应用背景和方法[J]. 地球信息科学学报, 2002, 4(1): 98-103.