Course Title : Remote Sensing and Geographic Information Systems

Code 10A805
Course Year Master and Doctor Course
Term 1st term
Class day & Period Tue 2nd
Location C1-117
Credits 2
Restriction No Restriction
Lecture Form(s) Lecture & Exercise
Language Japanese
Instructor Nobuhiro Uno and Junichi Susaki

Course Description

Geoinformatics is the science and technologies dealing with spatially distributed data acquired with remote sensing, digital photogrammetry, global positioning system, etc, to address the problems in natural phenomena or human activities. This course particularly focuses on remote sensing by using LiDAR and geographic information system (GIS) and explains the theory and applications. Unlike traditional surveying, LiDAR technique can sequentially obtain the data in a wide area within a short time, and thus it is now widely used in construction and management of civil infrastructure. GIS is a technique to handle digital maps and related information, and it is popular in the fields of urban planning, environmental management and infrastructure management. This course provides an understanding of remote sensing and GIS via applications presented by the exercises of remote sensing and lectures of GIS.

Grading

Grading is based on the achievements in exercise and assignments.

Course Goals

Students understand the basic theory and acquire the basic techniques of remote sensing for observation and analysis of environmental changes, disaster effects and human activities in urban areas. And, they understand the basic theory and applications of GIS.

Course Topics

Theme Class number of times Description
Object extration and landscape analysis from LiDAR data 1 The principle of Light detection and ranging (LiDAR) and the method to generate digital surface model (DSM) from point clouds are explained. As applications of LiDAR data, methods to extract objects by using geometric features and estimate landscape indices are introduced.
(Exercise) Field measurement by using LiDAR 2 Field measurement by using LiDAR is conducted in Katsura Campus.
(Exercise) Co-registration of LiDAR data and its assessment 1 LiDAR data are co-registered and its accuracy is assessed.
(Exercise) Vegetation extraction from LiDAR data and green space ratio estimation 1 Vegetation is extracted by using scattergram of point clouds. Green space ratio from an arbitrary viewpoint is calculated, and the vegetation landscape is assessed.
Satellite remote sensing 1 Basic terms on electromagnetic radiation including radiation and reflection are introduced, and calculation of suface reflectance and temperature is explained. In addition, principles and applications of visible and infrared sensors are introduced.
(Exercise) Vegetation coverage ratio estimation from satellite images 1 Vegetation index is calculated from an optical satellite image, and vegetation coverage ratio is estimated.
Introduction to GIS 1 Structure of GIS (Geographic Information System) and its utilization for spatial analysis are outlined.
GIS and Network Analysis 1 Basic idea of network structure, evaluation indices and methods of network analysis are explained.
GIS and Spatial Correlation Analysis 1 Focusing on spatial correlation analysis useful for developing spatial model, regression analysis and spatial auto correlation analysis are explained.
Classification Method of Spatial Attribute 1 Classification method of spatial attribute is explained in order to classify the target area using attribute information in GIS.
Transportation Big Data Collected by Mobile Objects Observation and Its Utilization 1 The changes in transportation observation led by progress of location identification technologies is stated. In addition, utilizations and issues of big data in transportation are explained.
Realization of Smart City and Big Data Utilization 1 The concept of Smart City and corresponding projects are introduced, and utilization and issues of big data for smart city are explained.
Analyses of Big Data 1 Analysis methods to utilize information of big data are explained. Especially, multivariate analysis and machine learning are outlined.
Assessment of understanding 1 Assess students' understanding levels

Textbook

Textbook(supplemental)

- Junichi Susaki and Michinori Hatayama, Geoinformatics, Corona Publisher, 2013
- W. G. Rees,Physical Principles of Remote Sensing 3rd ed., Cambridge University Press, 2013.
- J. A. Richards and X. Jia,Remote Sensing Digital Image Analysis: An Introduction, 5th ed., Springer-Verlag, 2013.
-M. Netler and H. Mitasova, Open Source GIS: A GRASS GIS Approach 3rd ed., The International Series in Engineering and Computer Science, 2008.

Prerequisite(s)

Independent Study Outside of Class

Web Sites

http://www.gi.ce.t.kyoto-u.ac.jp/user/susaki/rsgis/index.html

Additional Information

Students may be required to use their own laptop computer for exercise. Two exercises offered in the 1st and 2nd hour in a row are planned in April.