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Landslides are one of the critical natural hazards that cause human, infrastructure, and economic losses. Risk of catastrophic losses due to landslides is significant given sprawled urban development near steep slopes and the increasing proximity of large populations to hilly areas. For reducing these losses, a high-resolution digital terrain model (DTM) is an essential piece of data for a qualitative or a quantitative investigation of slopes that may lead to landslides. Data acquired by a terrestrial laser scanning (TLS), called a point cloud, has been widely used to generate a DTM, since a TLS is appropriate for detecting small-to large-scale ground features on steep slopes. For an accurate DTM, TLS data should be filtered to remove non-ground points, but most current algorithms for extracting ground points from a point cloud have been developed for airborne laser scanning (ALS) data and not TLS data. Moreover, it is a challenging task to generate an accurate DTM from a steep-slope area by using existing algorithms. For these reasons, we developed an algorithm to automatically extract only ground points from the point clouds of steep terrains. Our methodology is focused on TLS datasets and utilizes the adaptive principal component analysis-triangular irregular network (PCA-TIN) approach. Our method was applied to two test areas and the results showed that the algorithm can cope well with steep slopes, giving an accurate surface model compared to conventional algorithms. Total accuracy values of the generated DTMs in the form of root mean squared errors are 1.84 cm and 2.13 cm over the areas of 5252 m 2 and 1378 m 2 , respectively. The slope-based adaptive PCA-TIN method demonstrates great potential for TLS-derived DTM construction in steep-slope landscapes.

With the incorporation of an automated fare-collection system into the management of public transportation, not only can the quality of transportation services be improved but also that of the data collected from users when coupled with smart-card technology. The data collected from smart cards provide opportunities for researchers to analyze big data sets and draw meaningful information out of them. This study aims to identify the relationship between travel patterns derived from smart-card data and urban characteristics. Using seven-day transit smart-card data from the public-transportation system in Seoul, the capital city of the Republic of Korea, we investigated the temporal and spatial boarding and alighting patterns of the users. The major travel patterns, classified into five clusters, were identified by utilizing the K-Spectral Centroid clustering method. We found that the temporal pattern of urban mobility reflects daily activities in the urban area and that the spatial pattern of the five clusters classified by travel patterns was closely related to urban structure and urban function; that is, local environmental characteristics extracted from land-use and census data. This study confirmed that the travel patterns at the citywide level can be used to understand the dynamics of the urban population and the urban spatial structure. We believe that this study will provide valuable information about general patterns, which represent the possibility of finding travel patterns from individuals and urban spatial traits.