EXTRACTION OF RAILROAD OBJECTS FROM VERY HIGH RESOLUTION HELICOPTER-BORNE LIDAR AND ORTHO-IMAGE DATA

LiDAR (Light Detection and Ranging) sensors and digital aerial camera systems using a slow and low flying aircraft provide a new quality of data for a variety of promising large-scale applications. The main of this study objective is the development of methods for the automated object extraction of railway infrastructure from combined helicopter-based extremely dense laser scanner measurement points and very high resolution digital ortho-imagery. Thus, different existing methods from digital image processing, image segmentation and object recognition have been compared regarding their performance, output quality and level of automation. It turned out that all existing methods are not suitable to meet the requirements (geometrical accuracy of the result, amount of data to be processed etc.). Since original LiDAR point data provides a higher accuracy than derived DTM raster data or ortho-imagery new suited methods for the object extraction from point clouds have been developed. For the extraction of linear features, such as rails and catenaries, two new methods were implemented. The first method sets up on pre-classified laser points as input data. Therefore the RANSAC algorithm was implemented successfully to extract linear objects within the environment of MATLAB and ArcGIS. Second, a knowledge-based classification method was designed to compare a reference profile with the situation along the track using IDL. The results show new prospects to automatically extract railroad objects with a high geometrical accuracy from extremely dense LiDAR data without using aerial imagery. The decision not to use image data was especially caused by the enormous data amount t

High field superconducting properties of Ba(Fe1−xCox)2As2 thin films

In general, the critical current density, J(c), of type II superconductors and its anisotropy with respect to magnetic field orientation is determined by intrinsic and extrinsic properties. The Fe-based superconductors of the ‘122’ family with their moderate electronic anisotropies and high yet accessible critical fields (H(c2) and H(irr)) are a good model system to study this interplay. In this paper, we explore the vortex matter of optimally Co-doped BaFe(2)As(2) thin films with extended planar and c-axis correlated defects. The temperature and angular dependence of the upper critical field is well explained by a two-band model in the clean limit. The dirty band scenario, however, cannot be ruled out completely. Above the irreversibility field, the flux motion is thermally activated, where the activation energy U(0) is going to zero at the extrapolated zero-kelvin H(irr) value. The anisotropy of the critical current density J(c) is both influenced by the H(c2) anisotropy (and therefore by multi-band effects) as well as the extended planar and columnar defects present in the sample