Huge number of manhole covers exists on road surface. Owners of manholes inspect periodically to see a level difference and wear of cover pattern. The current method of measuring level difference and height of the pattern involves the inspector setting up a working zone on the road then using a caliper to take measurements. This method is problematic in that it is necessary to set up a working zone to ensure the safety of the inspector. We propose a safe and efficient method based on image processing for automatically measuring the level difference and detecting worn surface.

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 Non-destructive inspection method for concrete lining was proposed by using lasers for excitation and detection. Laboratory tests were conducted to confirm the basic performance with the developed system and it applied to actual tunnel lining for bullet train. As a result, it is revealed that vibrations and noises generated from maintenance vehicles and electric generator cause adverse effects on the measurement accuracy. In order to reduce the vibrations and noises, a new vibration canceling optical system and sound proof room are developed and installed on the proposed system. Consequently, it was confirmed that the developed system can measure the vibration characteristics of concrete lining.

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 In this paper, we present a method to estimate green space ratio (GSR), that is a landscape index representing a proportion of green area to a whole view area, by using terrestrial light detection and ranging (LiDAR). First, the method extracts vegetation points out of the 3D point clouds. The method calculates the surface roughness in multi-scale voxels, and the vegetation is extracted by referring to the surface roughness and the voxel continuity. Then, GSR is estimated via occlusion map by examining the occlusion by vegetation. In this regard, the echo data representing the number of echoes and reflected timing (first, last, others) are utilized. We applied the method to thirteen data sets collected in a residential area in Fushimi, Kyoto and estimated GSRs. We compared the results with the ground truth data and obtained RMSE of approximately 1.6%. The estimated GSRs are reasonable to suppose that our method shows great improvement in the accuracy compared to the result with airborne LiDAR.

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 In bedrock construction projects such as dams and tunnels, it is important to have a detailed grasp of the geological situation at the project site and to perform planning and construction appropriate to the situation. To that end, in recent years there have been investigations of various construction information modeling (CIM) management methods that realize 3D modeling of the presumed geological situation and the results of actual geographic observations. This paper describes the creation of a construction site support system for geological risk, in particular issues related to geological information CIM management system development, system content, and application to actual tunnel and dam excavation projects.

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 Recently, infrastructure monitoring in developing countries is not enough, though it becomes very important based on the development of social capital. In this study, we asked to collect road state data, especially road bump and hourly traffic link speed from GPS and acceleration data, for the person in developing countries by smartphones using crowdsourcing technology, such as outsourcing market place “Freelancer” and utilization of hub person in these countries. Finally, we collected approximately total 7,800km namely actual link based 800km driving data in short periods such as 1 month and a half based on the hub person method. This length means about 38% coverage toward 2,140km as a total road length in Dhaka metropolitan urban area. This result shows the possibility of collection of infrastructure monitoring data with their quality in short periods.

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