• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.27 no.5
• /
• pp.619-628
• /
• 2009

LiDAR has capability of rapid data acquisition and provides useful information for reconstructing surface of the Earth. However, Extracting information from LiDAR data is not easy task because LiDAR data consist of irregularly distributed point clouds of 3D coordinates and lack of semantic and visual information. This thesis proposed methods for automatic extraction of buildings and 3D detail modeling using airborne LiDAR data. As for preprocessing, noise and unnecessary data were removed by iterative surface fitting and then classification of ground and non-ground data was performed by analyzing histogram. Footprints of the buildings were extracted by tracing points on the building boundaries. The refined footprints were obtained by regularization based on the building hypothesis. The accuracy of building footprints were evaluated by comparing with 1:1,000 digital vector maps. The horizontal RMSE was 0.56m for test areas. Finally, a method of 3D modeling of roof superstructure was developed. Statistical and geometric information of the LiDAR data on building roof were analyzed to segment data and to determine roof shape. The superstructures on the roof were modeled by 3D analytical functions that were derived by least square method. The accuracy of the 3D modeling was estimated using simulation data. The RMSEs were 0.91m, 1.43m, 1.85m and 1.97m for flat, sloped, arch and dome shapes, respectively. The methods developed in study show that the automation of 3D building modeling process was effectively performed.

• Economic and Environmental Geology
• /
• v.49 no.2
• /
• pp.121-134
• /
• 2016

We characterize the spatial distribution of Cobalt-rich ferromanganese crusts covering the summit and slopes of a seamount in the western Pacific, using acoustic backscatter from multibeam echo sounders (MBES) and seafloor video observation. Based on multibeam bathymetric data, we identify that ~70% of the summit area of this flattopped seamount has slope gradients less than $5^{\circ}$. The histogram of the backscatter intensity data shows a bi-modal distribution, indicating significant variations in seabed hardness. On the one hand, visual inspection of the seafloor using deep-sea camera data exhibits that the steep slope areas with high backscatter are mainly covered by manganese crusts. On the other hand, the visual analyses for the summit reveal that the summit areas with relatively low backscatter are covered by sediments. The other summit areas, however, exhibit high acoustic reflectivity due to coexistence of manganese crusts and sediments. Comparison between seafloor video images and acoustic backscatter intensity suggests that the central summit has relatively flat topography and low backscatter intensity resulting from unconsolidated sediments. In addition, the rim of the summit and the slopes are of high acoustic reflectivity because of manganese crusts and/or bedrock outcrops with little sediments. Therefore, we find a strong correlation between the acoustic backscatter data acquired from sea-surface multibeam survey and the spatial distribution of sediments and manganese crusts. We propose that analyzing acoustic backscatter can be one of practical methods to select optimal minable areas of the ferromanganese crusts from seamounts for future mining.