Integration of multi-stage remote sensing data and geospatial information with the
cloud computing service for rapid response to natural disasters in Taiwan
Cheng-Chien Liu1,2,3,4*, Chih-Hua Chang4,6, Mon-Lin Chou1, Jen-Shin Hong5 and FanChun Hsieh4
1
Institute of Satellite Informatics and Earth Environment, National Cheng Kung
University, Tainan, Taiwan 701 R.O.C.
2
Department of Earth Sciences, National Cheng Kung University, Tainan, Taiwan 701
R.O.C.
3
Earth Dynamic System Research Center, National Cheng Kung University, Tainan,
Taiwan 701 R.O.C.
4
Global Earth Observation and Data Analysis Center, National Cheng Kung University,
Tainan, Taiwan 701 R.O.C.
5
Department of Environmental Engineering, National Cheng Kung University, Tainan,
Taiwan 701 R.O.C.
6
Department of Computer Science and Information Engineering, National Chi Nan
University, Taiwan R.O.C.
*
E-mail: ccliu88@mail.ncku.edu.tw
Abstract
Climate changes caused by global warming has reshaped the weather patterns all around
the world. As a result, the natural disasters triggered by extreme weather events have
threatened more and more properties and lives in many countries, particularly in Taiwan.
Taiwan is located in the Circum-Pacific seismic belt with the sub-tropical monsoon
climate. The high mountains, broken terrain and frequent earthquakes, together with the
heavy rainfall during the rainy and typhoon seasons, causes more and more geohazards of
landslides and debris flow in Taiwan area (Liu et al., 2010). To enhance the capability of
disaster response and mitigation, remotely sensed imagery and geospatial information
have been systematically collected national-wide in the past two decades, using a variety
of multi-stage platforms and sensors. To integrate and distribute such huge amount of
data, however, has become a new challenge for the data managers.
The emerge of Google Earth in 2005 has driven public interest in geospatial technologies
and applications. Apart from the large geospatial database that can be freely accessed
through the web-based Google Earth, the Application Programming Interface (API)
provided by Google enables us to develop a customized system that can integrate and
distribute all kinds of geospatial data. Together with the rapid progress of cloud service in
the past few years, the web-based system now can be browsed and explored by a lot of
users simultaneously without any lag. This motivates us to develop a web-based system
dedicated for rapidly responding to the disaster events, using Google Earth API and
deploying through the cloud service.
This paper describes the Multi-stage remote sensing data and geospatial information
system (MRSDGIS) that we developed for eight towns in southern Taiwan using Google
Earth API. A total of four types of remote sensing imagery, including Formosat-2
satellite images, aerial photos, UAV photos and panorama images, are collected from the
multi-stage platforms ranging from space, mid-altitude, low-altitude and ground. The
geospatial layers that can be overlaid are 1:25,000 base maps, land cover/use maps,
geology maps (including faults and folds), as well as the vectors of boundaries, roads,
rivers. To facilitate the rapid response and mitigation of natural disasters, all related
geospatial information have been integrated into our system as well, including debrisflow-prone streams, debris-flow-potential-hazard areas, potential inundation maps, dip
slops, river bank erosion, basins and sub catchments, debris flow protected objects,
landslides inventory, and the real-time precipitation. Since the original sizes of all raster
data are too large, we apply the technique of superoverlay to build the pyramid structures
for all data, following the standard kml/kmz format specified by Google Earth. We
successfully deploy our MRSDGIS using various cloud services. Further works would be
running a series of tests to evaluate the efficiency and cost of various cloud services.