Major Research Subjects of Shibasaki, R. (1998-) Professor
Further information is available at http://shiba.iis.u-tokyo.ac.jp/en/index.html
His research interests cover spatial data acquisition techniques
of a real world, data model development and their applications
especially in environmental fields in conjunction with agent-based
models. Major targets of data acquisition and modeling are such
fundamental data that can support diversified data uses as social
"infrastructure" or spatial data infrastructure (SDI).
The research is also extending to design or planning methodologies
to determine what kinds or types of spatial data with what level
of quality should be shared and developed as spatial data infrastructure
(SDI). Target scales of spatial data are two-folds; street-block
to city scale and continental to global scale.
Major research topics are summarized as follows;
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Development of sensor systems and algorithms for automated
spatial data acquisition
1) Automated acquisition of 3D spatial data at street-block to
city scale
Sensor systems and data processing methods are developed to automate
data acquisition and model-building of 3D spatial data of urban
features (geographical objects) at street block to city level.
Sensor system development focuses on how to integrate what kinds
of different sensors such as laser scanners and high resolution
linear CCD sensors, while the development of data processing methods
targets data fusion of those different sensors for automated feature
(geographical objects) extraction and modeling. Examples of the
sensor systems under development include air-borne Three Line
Sensor (TLS) and vehicle-borne laser mapping system (VLMS). Only
one system exists similar to the TLS, which is developed by DLR
independently. For VLMS, there are no equivalent systems. Data
fusion methods for those sensor systems include registration/merging
of image data acquired independently and extraction and reconstruction
of 3D features such as buildings, roads, automobiles and trees
from those image data.
2) Development of sensor systems and data processing methods for
observing moving objects such as pedestrians and automobiles.
Sensor systems for moving objects include laser scanners and positioning
devices consisting of wearable sensors such as accelerometer gyro
and barometer, which enables to measure the trajectory and behavioral
patterns of not only individual persons or automobiles but also
their group. In addition, possibility of Pseudolite (Pseudo satellite),
a kind of transceiver which emits signal similar to GPS's, is
explored through field experiment with urban environment to achieve
"seamless" positioning from indoors to outdoors. By
combining Pseudolite with the laser scanners and the wearable
positioning devices mentioned above, human behavior can be monitored
in 3D urban space.
3) Automated spatial data development using satellite remote sensing
at continental to global scale.
Algorithms of handling satellite remote sensing images are developed
to generate spatial datasets such as DEM (digital elevation model),
land cover dataset, temperature/precipitation datasets at continental
to global scale.
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Design methodologies for spatial data infrastructure (SDI)
"Spatial data infrastructure (SDI)" should be designed
as such that can support the development of as many applications
as possible as a commonly shared dataset. Selection/definition
of features, design of their scheme and evaluation of cost-effectiveness
of SDI development should be conducted based on systematic survey
and analysis of information usage of diversified user groups.
A design supporting methodology is being developed including surveying
methods of information usage, an organizing method to extract
features and their logical structures referred by diversified
user groups. This methodology is actually used to design a common
road data model to support national road administration and to
extract 3D road and underground/indoor space data for supporting
ITS service for pedestrians.
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Reconstruction of spatio-temporal changes of real world
phenomena
Many real world phenomena such as traffic congestion and atmospheric
conditions change spatially and dynamically. However, it is usually
very difficult to observe those changes continuously in both space
and time. It creates necessity to reconstruct spatio-temporal
changes from fragmentary observational data. Reconstruction accuracy
can be improved by incorporating knowledge or models on behavior
or dynamics of object-phenomena such as aero-dynamic equations
for atmospheric conditions. In addition to the development of
a conceptual model representing dynamic spatial data including
observation and event data with uncertainties, reconstruction
methods based on GA (Genetic Algorithm) is developed. The method
is applied to reconstruct a very long-term land use/land cover
change of the Earth and human movement in urban areas.
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Development of spatial agent models
As detailed spatial information of diversified objects including
moving objects in 3D urban scene can be acquired, a foundation
can be formed to model behavior of individual persons and automobiles
in a space as agents. It is suggested that the dynamics of traffic
congestion and movement of crowds can be more faithfully represented
by agent-based models(ABM). ABM (AGENT-LUC) is developed to model
land use changes in Thailand and Laos focusing on agricultural
land use changes due to population growth and deforestation by
slash-and-burn agriculture. The model also contributes to identifying
advantages and limitations of the approach and research challenges
in linking with spatial databases and in implement using parallel
processing. The AGENT-LUC also represents crops as an agent growing
based on agricultural operations such as water management and
fertilizer applications.
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