An automated and efficient method for extraction of tunnel cross sections using terrestrial laser scanner (TLS) data is presented. In this process, a three-dimensional (3D) point cloud acquired from the TLS is projected onto a horizon plane, converted to a two-dimensional (2D) planar image, and skeletonized to estimate the tunnel centerline. Stations from which cross sections are extracted are estimated from that centerline, which is vectorized and smoothed for better estimation. From those stations, cross-sectional planes are estimated, and point groups surrounding the planes are extracted. By using these point groups, the azimuth angles of the cross-sectional planes are adjusted, and the final cross sections are generated by projecting the nearby points to the adjusted planes. The performance of the proposed method was evaluated by applying it to a real tunnel and comparing the results with those from a conventional method that uses a total station. In the results, the cross sections were extrac...

Automated three-dimensional (3D) modeling of building interiors for an as-built building information model (BIM) incurs critical difficulties because of the complex design of indoor structures and a variety of clutter from scanned point clouds. This paper proposes a scheme for automated 3D geometric modeling of indoor structures, including detailed components such as windows and open doors. Moreover, to produce a regularized model, we imposed constrained least-squares adjustment according to an assumption made with respect to typical indoor structures: walls, ceiling, floor, doors, and windows are composed of straight lines, either parallel or orthogonal. Modeling proceeds in two main phases: wall modeling and window modeling. In the wall modeling phase, the point-cloud acquisitions are projected onto a 2D binary image and the wall boundary is traced out. The boundary is regularized by means of the constrained least-squares method, and, thereby, a 2D floor boundary map can be obtained that later p...

The growing interest and use of indoor mapping is driving a demand for improved data-acquisition facility, efficiency and productivity in the era of the Building Information Model (BIM). The conventional static laser scanning method suffers from some limitations on its operability in complex indoor environments, due to the presence of occlusions. Full scanning of indoor spaces without loss of information requires that surveyors change the scanner position many times, which incurs extra work for registration of each scanned point cloud. Alternatively, a kinematic 3D laser scanning system, proposed herein, uses line-feature-based Simultaneous Localization and Mapping (SLAM) technique for continuous mapping. Moreover, to reduce the uncertainty of line-feature extraction, we incorporated constrained adjustment based on an assumption made with respect to typical indoor environments: that the main structures are formed of parallel or orthogonal line features. The superiority of the proposed constrained adjustment is its reduction for uncertainties of the adjusted lines, leading to successful data association process. In the present study, kinematic scanning with and without constrained adjustment were comparatively evaluated in two test sites, and the results confirmed the effectiveness of the proposed system. The accuracy of the 3D mapping result was additionally evaluated by comparison with the reference points acquired by a total station: the Euclidean average distance error was 0.034 m for the seminar room and 0.043 m for the corridor, which satisfied the error tolerance for point cloud acquisition (0.051 m) according to the guidelines of the General Services Administration for BIM accuracy.

The reflectance of the Earth’s surface is significantly influenced by atmospheric conditions such as water vapor content and aerosols. Particularly, the absorption and scattering effects become stronger when the target features are non-bright objects, such as in aqueous or vegetated areas. For any remote-sensing approach, atmospheric correction is thus required to minimize those effects and to convert digital number (DN) values to surface reflectance. The main aim of this study was to test the three most popular atmospheric correction models, namely (1) Dark Object Subtraction (DOS); (2) Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH) and (3) the Second Simulation of Satellite Signal in the Solar Spectrum (6S) and compare them with Top of Atmospheric (TOA) reflectance. By using the k-Nearest Neighbor (kNN) algorithm, a series of experiments were conducted for above-ground forest biomass (AGB) estimations of the Gongju and Sejong region of South Korea, in order to check the effectiveness of atmospheric correction methods for Landsat ETM+. Overall, in the forest biomass estimation, the 6S model showed the bestRMSE’s, followed by FLAASH, DOS and TOA. In addition, a significant improvement of RMSE by 6S was found with images when the study site had higher total water vapor and temperature levels. Moreover, we also tested the sensitivity of the atmospheric correction methods to each of the Landsat ETM+ bands. The results confirmed that 6S dominates the other methods, especially in the infrared wavelengths covering the pivotal bands for forest applications. Finally, we suggest that the 6S model, integrating water vapor and aerosol optical depth derived from MODIS products, is better suited for AGB estimation based on optical remote-sensing data, especially when using satellite images acquired in the summer during full canopy development.

Diverse approaches to laser point segmentation have been proposed since the emergence of the laser scanning system. Most of these segmentation techniques, however, suffer from limitations such as sensitivity to the choice of seed points, lack of consideration of the spatial relationships among points, and inefficient performance. In an effort to overcome these drawbacks, this paper proposes a segmentation methodology that: (1) reduces the dimensions of the attribute space; (2) considers the attribute similarity and the proximity of the laser point simultaneously; and (3) works well with both airborne and terrestrial laser scanning data. A neighborhood definition based on the shape of the surface increases the homogeneity of the laser point attributes. The magnitude of the normal position vector is used as an attribute for reducing the dimension of the accumulator array. The experimental results demonstrate, through both qualitative and quantitative evaluations, the outcomes’ high level of reliability. The proposed segmentation algorithm provided 96.89% overall correctness, 95.84% completeness, a 0.25 m overall mean value of centroid difference, and less than 1° of angle difference. The performance of the proposed approach was also verified with a large dataset and compared with other approaches. Additionally, the evaluation of the sensitivity of the thresholds was carried out. In summary, this paper proposes a robust and efficient segmentation methodology for abstraction of an enormous number of laser points into plane information.

An enormous amount of transit data, created when people use their transportation cards, is being used effectively as smart card-based fare payment systems are being widely implemented. In Korea, the transportation card was first introduced by the Seoul Special City government in 1996 on its local bus services and has been compatible with the metropolitan subway system since 2000. Public transit card data can be used for many studies including determining the flow of transit passengers, which makes it possible to establish effective land development and transportation policies at a national level. In this paper, we aim to identify the daily travel patterns of metropolitan subway passengers as they move through stations using clustering analysis and reveal the relationship between land cover and demographic factors by using regression analysis. The main functions are confirmed in accordance with the intended use of each station and classified into five types. Moreover, we identified the features and influencing factors of the station influence area utilizing demographic data and a land cover map. The results obtained by clustering and regression analysis showed the clear relationship between travel patterns and the other characteristics in the Seoul Metropolitan Area. The results of this study could be important for national development strategies and traffic planning.