The spatial density of the seismometers was not high enough to obtain an adequate spatial distribution of the earthquake ground motion. To interpolate the network of seismometers, we propose a method to estimate earthquake ground motion by using a surveillance camera. In our method, the motion of objects sliding on the ground or on a floor is estimated by image analysis. Then, the ground motion is obtained by inverse analysis of the sliding motion. We conducted scale model experiments to evaluate our method, and the experimental results show the effectiveness of our method.

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 Point-clouds captured by a Mobile Mapping System (MMS) are useful for maintenance of infrastructures, such as utility poles and road signs. To use point-clouds in many applications, it is necessary to identify feature types of each point-set. We think that machine learning techniques are promising for this purpose. In our research, we consider geometric properties of a point-set that are effective for machine learning. Since there are a lot of pole-like objects to be repaired along roads, we investigate geometric properties of pole-like objects. Although a lot of geometric properties can be considered for point-sets, the effectiveness of geometric properties has not been quantitatively evaluated so far. We use the contribution degree of machine learning to identify effective geometric properties for classification of pole-like objects. Our experimental result showed that our method could effectively extract useful geometric properties that are calculated using a point-set.

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 Three-dimensional point cloud data is widely used, and the applications of the data are being studied actively in various research fields. In our previous research, we have illustrated an analysis method to set the data attribute of the structure which is located in road and roadside. We have also built an editing system that gives the attributes to point cloud data manually, and shown how to use the point cloud data using the system. In this study, we have used edited point cloud data in three studied cases, and shown the possibility of using point cloud data in various consideration and discussion.

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 For better image-texture analysis and interpretation of moving images used in the construction field, a real-time VISibility evaluation system (VIS system) for "Feature Composite Moving (FCM) images" inducing visual illusion (i.e., pseudo-rotational and persistent of vision) has been developed. The animation-like FCM images are produced by continuously displaying emboss processing-based images (i.e., shade images) according to eight directions of light sources. The power spectra for the fluctuation of digital number values (i.e., spatial frequency components) in each pixel of FCM image are grouped into 40 classes which are delineated on "Visibility Evaluation (VE) map." VIS system can execute those image processing in real time, which contributes to improve the accuracy of quantitative evaluation of visibility for various kinds of texture features in moving images.

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 Exterior deformation is one of the most important measurements for the safety management of embankment dams. Conventional survey methods, such as leveling and total station surveys, have been adopted for this purpose. However, these human-operated methods involve high labor costs in the long run. In addition, just after serious disasters, immediate surveys might be difficult and dangerous in some cases.
 To overcome these problems, the authors have been promoting an automatized displacement monitoring method using the Global Positioning System (GPS), which monitors the three-dimensional displacements of multiple measurement points remotely and continuously. Although our previous studies proved its effectiveness, a problem has been revealed, namely, that normal GPS antennas, set at the dam crest, could not measure the deformation of the embankment core accurately.
 Therefore, a new method has been developed in the present study for monitoring the core displacements using antennas, which are installed inside of the manhole at the dam crest. This paper describes the results of the performance tests of the proposed method and discusses the applicability for its practical use.

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 A tank model is widely used to make a run-off analysis or a prediction of the land-slide disaster occurrence. To obtain accurate simulation results it is necessary to identify a model structure and its model parameters suitably. However, it is difficult to identify the model structure and parameters simultaneously. The conventional manner to construct the tank model is that the model structure is determined in advance and then the model parameters are identified. The identification of the tank structure has not discussed sufficiently. In this study, we have attempted to identify the model structure and the model parameters with SCE-UA method which is one of the optimization methods. The identification in the model has been conducted to the Saba River basin, Yamaguchi Prefecture. It is found that the obtained three tanks model can predict the observation data with the nearly same precision as the complex five tanks model.

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