To promote rapid and efficient safety management of filldams, this study examined the applicability of interferometry SAR (InSAR), which is used to measure exterior deformation of filldams using ALOS PALSAR data. The result showed the accuracy, Root Mean Square Error, of displacement of filldams monitored by InSAR compared to GPS measurement was about 11～13mm. In addition, the trend of time-series variation was well captured by InSAR. Therefore, this study indicated that it is possible to monitor exterior deformation of filldams by InSAR techniques using ALOS PALSAR data. Moreover, there is a possibility of applying this technique to other large-scale infrastructures.
The revetment in Ota-gawa River in Hiroshima is long structure and it also has many damaged parts, so a work of survey and evaluation, especially visual inspection is spent huge cost and time. An urgent task is the development and application of efficient and easy inspection method. Therefore, we tried to survey and inspect the revetment by Structure from Motion with the Unmanned Aerial Vehicle. We verified about measuring range, shooting procedure measuring accuracy, the time needed to measure and compared the result of this trial, and the last soundness evaluation. Based on their results, we summarized the effectiveness and problems of inspection with UAV for the future application.
This technology is intended to obtain cross-sectional shapes and positional information of underground pipes which cannot be targeted as are open spaces on the ground (in case of spaces where GPS coordinates cannot continuously be acquired). For cross-sectional shapes, point cloud data is acquired by a laser scanner in a transverse direction relative to an object. For positional information, the self-location and position of the robot are estimated by odometric data collected via IMU, and the estimate value is corrected via shape matching by ICP (Iterative Closest Point) algorithms using the laser scanner in longitudinal and horizontal directions relative to an object. To improve accuracy, the correct coordinate value is given to the robot at the starting and end points, and correction of whole measuring section is conducted in coordination with GraphSLAM.
This study proposed a method to improve the geometric accuracy of the Advanced Himawari Imager (AHI), an instrument on board the recently launched Himawari-8 spacecraft, focusing on the data set over Japan acquired in the regional acquisition mode. The AHI scanning mechanism consists of four west-east horizontal sweeps, and derived four sub-images are combined into a single dataset. This scanning mechanism gives rise to variations in geometric accuracy among the four sub-images. These variations were quantified by analyzing the AHI band-3 images, which has the highest spatial resolution (0.5km) among the AHI bands. The geometric error, determined as the root mean square of average errors in each scan, was found to be approximately 0.3 pixels in the vertical (north-south) direction and 1 pixel in the horizontal direction during 8 hours of daytime. The geometric error showed two characteristics : temporal fluctuation, and variation by the scan. This study proposes a technique to improve these ‘scan-dependent' geometric errors by shifting the four sub-images independently based on the average error in each scan. The proposed method successfully reduced the geometric errors to 0.07 pixels in the vertical and 0.15 pixels in the horizontal direction, and hence improved the quality of time series composite images obtained using the AHI. Such geometrically-improved AHI image would enhance the accuracies of multi-temporal analyses used in the monitoring of vegetation, land use/land cover change, disasters and so on.
Development of high performance CPU, cameras and other sensors on mobile devices have been used for wide variety of applications. The applications require self-localization of the devices. Since the self-localization is based on GPS, gyro sensor, acceleration meter and magnetic field sensor (POS) of low accuracy, the applications are limited. On the other hand, self-localization method using images have been developed, and the accuracy of the method is increasing. This paper develops the self-localization method by integrating sensors, such as POS and a camera on a mobile device. The proposed method mainly consists of two parts : one is the accuracy improvement of POS data with filtering, and another is development of self-localization method by integrating POS data and images. The POS data filtering combines all data by using Kalman filter. The estimated exterior orientation factors are used as initial value of ones in image-based self-localization method, which is based on structure from motion. The exterior orientation factors are integrated and updated by applying bundle adjustment. Through experiments with real data, the accuracy improvement by the proposed method is confirmed.
We propose a method to verify the possibility of the evacuation range in the consideration of population distribution and capacity of refuge shelter when the tsunami occurred. In the proposed method, we use the road network data, building data, population mesh data, and DEM (Digital Elevation Model). Evacuation range is represented by meshes that are created by dividing population mesh data. In order to validate the proposed method, we conduct experiments in Misa Oita-shi.