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Using airborne full-waveform LiDAR metrics derived by 3-D tree segmentation, this study estimated single tree's diameter at breast height (DBH) and stem volume (STV). Four regression models were used, including multilinear regression and three up-to-date regression models (i.e., least square boosting trees regression, random forest, and $\varepsilon$-support vector regression) from the machine learning...
Remote sensing image classification has been widely applied in many fields such as resource exploration, environmental monitoring and urban planning. Support Vector Machine (SVM) is adopted in our research, to classify two sets of SPOT-5 images of an urban area. In order to achieve high classification accuracies, the kernel function of the SVM classifier is selected beforehand. Furthermore, the kernel...
Airborne LiDAR (ALS) data are characterized by involving not only rich spatial but also temporal information. It is possible to extract moving vehicles with motion artifacts from single-pass airborne LiDAR data, based on which the velocity of vehicles can be derived. In this paper, a series of methods for velocity estimation of moving vehicles using airborne LiDAR data is presented and evaluated....
Instability of Slopes is a most common geological hazards in natural world. At present, slope stability evaluation depends largely on slope stability coefficient calculation. Analysis and evaluation of slope stability is a complex geotechnical problem because material composition, internal structures, water content, density of rock and soil mass are changed with surrounding circumstances and loads...
Multiple sequence alignment is widely applied to discover core conserved regions among query sequences. However, the major deficiency is that alignment accuracy is extremely sensitive to primary sequence identity, which causes alignment of low identity sequences difficult. We propose a feature-integrated model called feature-incorporated alignment (FIA) which integrates relevant biological characteristics...
Power distribution networks (PDNs) are designed and analyzed iteratively. Random walk is among the most efficient methods for PDN analysis. We develop in this paper an incremental and on-demand random walk to reduce iterative analysis time. During each iteration, we map the design changes as positive or negative random walks for observed nodes. To update PDN analysis result, we only need to apply...
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