• Journal of Geo-information Science
  • Vol. 22, Issue 1, 57 (2020)
Jiancheng LUO1、1、2、2, Tianjun WU3、3、*, Zhifeng WU4、4, Ya'nan ZHOU5、5, Lijing GAO1、1、2、2, Yingwei SUN1、1、2、2, Wei WU6、6, Yingpin YANG1、1、2、2, Xiaodong HU1、1、2、2, Xin ZHANG1、1、2、2, and Zhanfeng SHEN1、1、2、2
Author Affiliations
  • 1State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
  • 1中国科学院空天信息创新研究院 遥感科学国家重点实验室,北京 100101
  • 2University of Chinese Academy of Sciences, Beijing 100049, China
  • 2中国科学院大学,北京 100049
  • 3School of Geology Engineering and Geomatics, Chang'an University, Xi'an 710064, China
  • 3长安大学 地质工程与测绘学院,西安 710064
  • 4School of Geographical Sciences, Guangzhou University, Guangzhou 510006, China
  • 4广州大学地理科学学院,广州 510006
  • 5School of Earth Science and Engineering, Hohai University, Nanjing 211100, China
  • 5河海大学地球科学与工程学院,南京 211100
  • 6College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China
  • 6浙江工业大学计算机学院,杭州 310023
  • show less
    DOI: 10.12082/dqxxkx.2020.190462 Cite this Article
    Jiancheng LUO, Tianjun WU, Zhifeng WU, Ya'nan ZHOU, Lijing GAO, Yingwei SUN, Wei WU, Yingpin YANG, Xiaodong HU, Xin ZHANG, Zhanfeng SHEN. Methods of Intelligent Computation and Pattern Mining based on Geo-parcels[J]. Journal of Geo-information Science, 2020, 22(1): 57 Copy Citation Text show less
    Geo-parcels and coupling relationships of their TUPU
    Fig. 1. Geo-parcels and coupling relationships of their TUPU
    Intelligent computation model of geo-parcels based on three sub-models: zoning-stratified perception, spatiotemporal synergistically inversion, and multi-granular decision-making
    Fig. 2. Intelligent computation model of geo-parcels based on three sub-models: zoning-stratified perception, spatiotemporal synergistically inversion, and multi-granular decision-making
    Zoning-stratified perception model
    Fig. 3. Zoning-stratified perception model
    Spatiotemporal synergistically inversion model
    Fig. 4. Spatiotemporal synergistically inversion model
    Multi-granular decision-making model
    Fig. 5. Multi-granular decision-making model
    Pattern mining and progressive relation of two-layer graph-spectrum based on geo-parcels
    Fig. 6. Pattern mining and progressive relation of two-layer graph-spectrum based on geo-parcels
    Distribution pattern mining based on geo-parcels
    Fig. 7. Distribution pattern mining based on geo-parcels
    Experimental results of geo-parcels extraction with cultivated land in Xifeng County, Guizhou Province
    Fig. 8. Experimental results of geo-parcels extraction with cultivated land in Xifeng County, Guizhou Province
    Growth pattern mining based on geo-parcels
    Fig. 9. Growth pattern mining based on geo-parcels
    Experimental results of crop mapping based on geo-parcels in Xifeng County, Guizhou Province
    Fig. 10. Experimental results of crop mapping based on geo-parcels in Xifeng County, Guizhou Province
    Functional pattern mining based on geo-parcels
    Fig. 11. Functional pattern mining based on geo-parcels
    Experimental results of sugarcane planting suitability evaluation in Jiangzhou District of Chongzuo City, Guangxi
    Fig. 12. Experimental results of sugarcane planting suitability evaluation in Jiangzhou District of Chongzuo City, Guangxi
    Dynamic pattern mining based on geo-parcels
    Fig. 13. Dynamic pattern mining based on geo-parcels
    Jiancheng LUO, Tianjun WU, Zhifeng WU, Ya'nan ZHOU, Lijing GAO, Yingwei SUN, Wei WU, Yingpin YANG, Xiaodong HU, Xin ZHANG, Zhanfeng SHEN. Methods of Intelligent Computation and Pattern Mining based on Geo-parcels[J]. Journal of Geo-information Science, 2020, 22(1): 57
    Download Citation