Name SHI Zhongchao
Official Title Professor
Affiliation Geoinfomatics, Remote Sensing,Traffic engineering, land planning
Profile After obtained the bachelor’s degree and master’s degree in 1985 and 1988 from Wuhan Technical University of Surveying and Mapping, I came to the University of Tokyo in 1993 for Doctor research and got a PhD in engineering in 1996.
My main research interests focus on providing advanced technologies and solutions for realizing safe, secure, comfortable, smart and eco cities with the fusion of technologies of GIS (Geographic Information System), RS (Remote Sensing), GPS (Global Positioning System), IOT (Internet of Things), AI (Artificial Intelligence) and so on.
(1) Research on Mobile Mapping System (MMS) development
(2) Research on fusion of stereo images and laser data
(3) Research on algorithm development for automatically extracting houses and road traffic signs from stereo images
(4) Research on development of GPS-Supported Visual SLAM
(5) Research on development of remote sensing data fusion technologies for automatic classification and feature recognition
(6) Research on smart agriculture and smart city
Research Field(Keyword & Summary)
  1. (1) GIS Data acquisition and processing

    The technology and fusion processing algorithm based on multi-sensor data collaborative collection such as inertial navigation system, panoramic camera and laser scanner are proposed, which realizes the rapid positioning, automatic registration and accurate recognition of ground objects of multi-sensor data. This achievement is at the world's leading level, and the technology system developed has been widely used in smart agriculture construction, smart city road and streetscape data collection, and automatic driving information acquisition.

  2. (2) GPS-Supported Visual SLAM

    A framework for GPS-supported visual Simultaneous Localization and Mapping with Bundle Adjustment (BA-SLAM) using a rigorous sensor model in a panoramic camera was proposed in this research. The rigorous model does not cause system errors, thus representing an improvement over the widely used ideal sensor model. The proposed SLAM does not require additional restrictions, such as loop closing, or additional sensors, such as expensive inertial measurement units

  3. (3) Remote sensing data fusion

    Accurate crop distribution maps provide important information for crop censuses, yield monitoring and agricultural insurance assessments. Most existing studies apply low spatial resolution satellite images for crop distribution mapping, even in areas with a fragmented landscape. Unmanned aerial vehicle (UAV) imagery provides an alternative imagery source for crop mapping, yet its spectral resolution is usually lower than satellite images. In order to produce more accurate maps without losing any spatial heterogeneity, this study fuses Sentinel-2A and UAV images to map crop distribution at a finer spatial scale in an experimental site with various cropping patterns.

Representative Papers
  1. (1) Recommending Tourism Attractions Based on Segmented User Groups and Time Contexts, Data Analysis and Knowledge Discovery, 2020, Vol.41, No. 5, pp92-104.
  2. (2)Finer classification of crops by fusing UAV images and Sentinel-2A data, Remote Sensing, 2019, Vol. 11, No. 24, pp1-17
  3. (3)Research Trends Evaluation for Agricultural Land Resource Remote Sensing, China Agricultural Informatics, Vol.31, No. 3, June, 2019, pp. 1-12.
  4. (4)Fusion of a panoramic camera and 2D laser scanner data for constrained bundle adjustment in GPS-denied environments, Image and Vision Computing, Vol. 40, June 2015, pp.28-37.
  5. (5)Particle filtering methods for georeferencing panoramic image sequence, ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 105, March 2015, pp.1-12.
  6. (6)Comparison of two panoramic sensor models for precise 3D measurements. Photogrammetric Engineering& Remote Sensing (PERS),Vol. 80, No.3, March 2014, p.229-238.
  7. (7)GPS-Supported Visual SLAM with a Rigorous Sensor Model for a Panoramic Camera in Outdoor Environments, Sensors, 13(1), Jan. 2013, pp.119–136.
  8. (8)Development of high-precision automatic position information acquisition method using vehicle-based multi-sensor data, Japan Society of Photogrammetry and Remote Sensing, Vol. 49, no. 2, 2010, pp.75-82.
  9. (9)A Method to Improve the Efficiency of the Lidar, Journal of Instruments, Vol.25, issue 4, 2004, pp.462-465.
  10. (10)Focal Plane Image Assembly of Subpixel, Geo-spatial Information Science, Vol. 5, issue 4, 2002, pp.55-59.
Grant-in-Aid for Scientific Research Support: Japan Society for Promotion of Science (JSPS)
Recruitment of research assistant(s) Yes (1 person)
Affiliated academic society (Membership type) Japan Society of Photogrammetry and Remote Sensing
Japan Association of Surveyors
Education Field (Undergraduate level) Image Processing, Geographic Information System, Environmental Monitoring Technology
Education Field (Graduate level) Urban Environment Modeling