The amount of bridge beam deflection is a key indicator for health monitoring of the railway structure. Most of current measurement of bridge beam deflection uses contact-type sensors which have difficulties in setting bridges hardly accessed. We propose describes non-contact type measurement of bridge beam deflection, where deflections are measured by analyzing motion in digital image sequences remotely captured by general digital camera. This paper introduces the method of calculating dynamic deflections from the photographic image and shows some measurement examples.
Recently, MMS (Mobile Mapping System) has been successfully used in a road maintenance. In this paper, we present the result of development of a basic technology to apply the data derived from MMS to the inspection and investigation of track and structures conducted in West Japan Railway Company. First, we obtained a point cloud around the track by MMS car for a road survey mounted on a bogie and checked acquired 3-D data of track and structures. Then, we developed an algorithm to extract rail track geometry from the point cloud and we analyzed the accuracy of extracting rail track geometry by applying this algorithm to the point cloud. Additionally we confirmed the accuracy of absolute position of the point cloud. Finally, we studied on how to apply a MMS point cloud to the inspection of railways, for example a clearance check.
Efficient survey method is necessary around many kinds of facilities in these days including indoor environment where GNSS for precise and convenient survey is not available. In this case we present the survey example of modern architectural heritage “THE MUSEUM MEIJI-MURA”with handheld laser scanner “Zeb1” using SLAM algorithm. We captured precise point cloud easily and at low cost. After adequate visualization the point cloud easily became 3D floor maps as well as floor plans and cross sections.
Since the beginning of the industrial revolution, the atmospheric concentration of carbon dioxide (CO2) has increased by nearly 41%, and the Methane (CH4) concentration has increased more than double. CH4 is the second most important greenhouse gas, following CO2. Emissions, extrapolated from measurements of actual gas flux from wetlands, vary from place to place, even within the same wetland. This high variability makes large-scale estimates difficult and means that average emissions levels include a large degree of estimated uncertainty. The SCIAMACHY instrument on the European Space Agency satellite ENVISAT measured greenhouse gases in the troposphere and stratosphere. In this study, the CH4 source area is extracted by estimating the emission as the difference between CH4 concentration in time series observed by SCIAMACHY and the background concentrations of CH4. Missing data of CH4 concentration by cloud are interpolated both spatially and temporally. It is assumed that CH4 concentration is negligible over ocean and that the CH4 concentration over the ocean is due to the advection of CH4 from the land. The background concentration of CH4 on the land was defined as CH4 concentration over the ocean in the same latitude. The estimated CH4 emissions from the land exhibited the source of CH4 are not only in paddy fields but also in broadleaf evergreen area in South America and Central Africa.
UAV (Unmanned Aerial Vehicle) photogrammetry, which combines UAV and freely available internet-based 3D modelling software, is widely used as a low-cost and user-friendly photogrammetric technique in the fields of photogrammetry, remote sensing and geosciences. In UAV photogrammetry, only the platform used in conventional aerial photogrammetry is changed. Therefore, 3D modelling software greatly contributes to the spread of UAV photogrammetry. However, the algorithms of the 3D modelling software are black-box. There are few literatures that evaluate the accuracy using check points that have 3D coordinates. With this motive, Smart3DCapture and Pix4Dmapper were downloaded from the Internet and commercial software (PhotoScan) was also employed; investigations were performed in this paper using check points and images obtained from UAV.