Rey-Jer You

A Quality Prediction Method for Building Model Reconstruction Using LiDAR Data and Topographic Maps

This paper integrates light detection and ranging (LiDAR) data and topographic maps and predicts the quality of 3-D building model reconstruction. In this paper, the tensor voting algorithm and a region-growing method are adopted to extract building roof planes and structural lines from LiDAR data, and a robust least squares method is applied to register LiDAR data with building outlines obtained from topographic maps. The minimal square sum of the separations of the most peripheral points to building outlines is adopted as the criterion for determining the transformation parameters in order to improve the efficiency of data fusion. After registration, a novel quality indicator of data fusion based on the tensor analysis of residuals is derived in order to evaluate the quality of the automatic reconstruction of 3-D building models. Finally, an actual LiDAR data set and its corresponding topographic map demonstrate the fusion procedure and the quality of the predictions related to automatic model reconstruction.

Optimal scale factor on the central meridian for a 2°TM map projection in TWD97

The purpose of this paper is to study the optimal scale factor on the central meridian in order to minimize distortions of the 2°Transverse Mercator map projection in TWD97. The optimal scale factor is first derived according to the Airy optimal theory of distortions. Next, the influences of the current and optimal scale factors on local length and area distortions are discussed and analyzed. The results show that the value of the optimal scale factor on the central meridian under consideration is 0.999942 and leads to minimal local distortions in both distances and areas on the island of Taiwan.

Caspian Sea level prediction using satellite altimetry by artificial neural networks

The demand for accurate predictions of sea level fluctuations in coastal management and ship navigation activities is increasing. To meet such demand, accessible high-quality data and proper modeling process are critically required. This study focuses on developing and validating a neural methodology applicable to the short-term forecast of the Caspian Sea level. The input and output data sets used contain two time series obtained from Topex/Poseidon and Jason-1 satellite altimetry missions from 1993 to 2008. The forecast is performed by multilayer perceptron network, radial basis function, and generalized regression neural networks. Several tests of different artificial neural network (ANN) architectures and learning algorithms are carried out as alternative methods to the conventional models to assess their applicability for estimating Caspian Sea level anomalies. The results derived from the ANN are compared with observed sea level values and with the forecasts calculated by a routine autoregressive moving average (ARMA) model. Different ANNs satisfactorily provide reliable results for the short-term prediction of Caspian Sea level anomalies. The root mean square errors of the differences between observations and predictions from artificial intelligence approaches can be significantly reduced by about 50xa0% compared with ARMA techniques.

Building Feature Extraction from Airborne Lidar Data Based on Tensor Voting Algorithm

This study presents a novel approach based on the tensor voting framework for extracting building features from airborne lidar data. Geometric features of lidar points are represented by a tensor field in this paper. For the extraction of roof patches, a region-growing method with principal features is developed from the properties of eigenvalues and eigenvectors of the tensor field. Additionally, three new indicators for the strength of features are presented to reduce the effect of the number of points on feature identification, and a supervised method is proposed to determine the threshold of planar feature strength for the region-growing. The extraction of ridge and edge lines from the segmented roof patches is also discussed. Experiments based on airborne lidar data are described to demonstrate the effectiveness of the proposed method, with those the results compared with the PCA method.

Adaptation of the torus and Rosborough approach to radial base functions

The most common approach for the processing of data of gravity field satellite missions is the so-called time-wise approach. In this approach satellite data are considered as a time series and processed by a standard least-squares approach. This approach has a very strong flexibility but it is computationally very demanding. To improve the computational efficiency and numerical stability, the so-called torus and Rosborough approaches have been developed. So far, these approaches have been applied only for global gravity field determinations, based on spherical harmonics as basis functions. For regional applications basis functions with a local support are superior to spherical harmonics, because they provide the same approximation quality with much less parameters. So far, torus and Rosborough approach have been developed for spherical harmonics only. Therefore, the paper aims at the development and testing of the torus and Rosborough approach for regional gravity field improvements, based on radial basis functions as basis functions. The developed regional Rosborough approach is tested against a changing gravity field produced by simulated ice-mass changes over Greenland. With only 350 parameters a recovery of the simulated mass changes with a relative accuracy of 5% is possible.

Analysis and prediction of Caspian Sea level pattern anomalies observed by satellite altimetry using autoregressive integrated moving average models

In this study, we successfully present the analysis and forecasting of Caspian Sea level pattern anomalies based on about 15xa0years of Topex/Poseidon and Jason-1 altimetry data covering 1993–2008, which are originally developed and optimized for open oceans but have the considerable capability to monitor inland water level changes. Since these altimetric measurements comprise of a large datasets and then are complicated to be used for our purposes, principal component analysis is adopted to reduce the complexity of large time series data analysis. Furthermore, autoregressive integrated moving average (ARIMA) model is applied for further analyzing and forecasting the time series. The ARIMA model is herein applied to the 1993–2006 time series of first principal component scores (sPC1). Subsequently, the remaining data acquired from sPC1 is used for verification of the model prediction results. According to our analysis, ARIMA (1,1,0)(0,1,1) model has been found as optimal representative model capable of predicting pattern of Caspian Sea level anomalies reasonably. The analysis of the time series derived by sPC1 reveals the evolution of Caspian Sea level pattern can be subdivided into five different phases with dissimilar rates of rise and fall for a 15-year time span.

Spatiotemporal Prediction of Satellite Altimetry Sea Level Anomalies in the Tropical Pacific Ocean

This letter developes and validates a machine learning approach to forecast sea level anomalies (SLAs) derived from satellite altimetry in the tropical Pacific Ocean. The empirical orthogonal function (EOF), also known as principal component analysis, was used to extract dominant signals and reduce the dimensionality of data sets. Such dimensionality was decreased by describing spatial patterns (EOFs) and the corresponding temporal domains [principal components (PCs)]. Support vector regression (SVR) was employed to predict the time series obtained from the leading PCs. Thereafter, the temporal and spatial SLAs from the proposed EOFs were reconstructed to represent the spatiotemporal SLA prediction. Finally, the prediction result was compared with that of the conventional autoregressive integrated moving average (ARIMA) model. Both models reached satisfactory sea level predictions. Even so, intercomparison of the obtained results showed that the SVR significantly (

From Riemann Manifolds to Riemann Manifolds

P = 0.012

“Sphere to Tangential Plane”: Polar (Normal) Aspect

) outperformed the ARIMA model in sea level forecasting. That is, a considerably low root-mean-square error was attained for the differences between the predicted and observed mean SLAs.

“Sphere to Tangential Plane”: Transverse Aspect

Mappings from a left two-dimensional Riemann manifold to a right two-dimensional Riemann manifold, simultaneous diagonalization of two matrices, mappings (isoparametric, conformal, equiareal, isometric, equidistant), measures of deformation (Cauchy–Green deformation tensor, Euler–Lagrange deformation tensor, stretch, angular shear, areal distortion), decompositions (polar, singular value), equivalence theorems of conformal and equiareal mappings (conformeomorphism, areomorphism), Korn–Lichtenstein equations, optimal map projections.