Automatic mapping of lunar landforms using DEM-derived geomorphometric parameters

Developing approaches to automate the analysis of the massive amounts of data sent back from the Moon will generate significant benefits for the field of lunar geomorphology. In this paper, we outline an automated method for mapping lunar landforms that is based on digital terrain analysis. An iterative self-organizing (ISO) cluster unsupervised classification enables the automatic mapping of landforms via a series of input raster bands that utilize six geomorphometric parameters. These parameters divide landforms into a number of spatially extended, topographically homogeneous segments that exhibit similar terrain attributes and neighborhood properties. To illustrate the applicability of our approach, we apply it to three representative test sites on the Moon, automatically presenting our results as a thematic landform map. We also quantitatively evaluated this approach using a series of confusion matrices, achieving overall accuracies as high as 83.34% and Kappa coefficients (K) as high as 0.77. An immediate version of our algorithm can also be applied for automatically mapping large-scale lunar landforms and for the quantitative comparison of lunar surface morphologies.     

Automated mapping of rock slope geometry,kinematics and stability with RSS-GIS

A GIS-implemented, deterministic approach for the automated spatial evaluation of geometrical and kinematical properties of rock slope terrains is presented. Based on spatially distributed directional information on planar geological fabrics and DEM-derived topographic attribute data, the internal geometry of rock slopes can be characterized on a grid cell basis. For such computations, different approaches for the analysis and regionalization of available structural directional information applicable in specific tectonic settings are demonstrated and implemented in a GIS environment. Simple kinematical testing procedures based on feasibility criteria can be conducted on a pixel basis to determine which failure mechanisms are likely to occur at particular terrain locations. In combination with hydraulic and strength data on geological discontinuities, scenario-based rock slope stability evaluations can be performed. For conceptual investigations on rock slope failure processes, a GIS-based specification tool for a 2-D distinct element code (UDEC) was designed to operate with the GIS-encoded spatially distributed rock slope data. The concepts of the proposed methodology for rock slope hazard assessments are demonstrated at three different test sites in Germany.     

利用HHT-EEMD方法分析云南区域GNSS应变时序孕震信息

地震的孕育和发生本质上都是地壳内部应力、应变能逐渐积累并突然或缓慢释放的结果,研究应变的变化过程对于地震危险性的判定具有重要意义。本文基于云南区域2013—2019年GNSS格网应变时间序列,利用专门适用于非线性非平稳信号处理的热门时频分析方法—整体经验模态分解（ensemble empirical mode decomposition,EEMD）的希尔伯特-黄变换（Hilbert-Huang transform,HHT）分析方法,探索云南区域中强地震前GNSS应变时序的时-频-能量分布特征,尝试挖掘应变时频信号中所携带的孕震信息。利用23号、42号格网对应的地震进行震例分析,结果显示：EEMD具有分频剖面的类似特征,它能够依据数据的时间特征尺度进行信号分解,较好地剖析信号在不同频率尺度上的变化特征;Hilbert变换能够通过瞬时频率、瞬时振幅等方式突出信号的局部瞬时特性,在固有模态分量（intrinsic mode functions,IMF）异常曲线识别无效的情况下仍能凸显异常;通过EEMD、残差趋势项分析、IMF异常识别和Hilbert变换综合动态分析应变时间序列的分析方法,能够在部分地震前夕发现一些潜在异常信息,为未来云南区域强震危险地点的判定提供一定的参考。     

多尺度特征融合深度学习建筑物的提取方法

提出一种基于多尺度特征融合的建筑物提取方法,结合新的网络DenseASPP-UNet,以实现影像多尺度特征的融合,进而高精度提取建筑物.通过Inria开源建筑物航空影像数据集进行验证,表明DenseASPP-UNet相比其他深度学习方法建筑物提取精度有很大的提升.     

基于GlobeLand30的全球城乡建设用地空间分布与变化统计分析

城乡建设用地分布与变化是人类活动的直观标志和生态足迹,在环境变化研究、地理国(世)情监测和可持续发展研究等方面发挥着重要作用。以往人们对一些城市、区域或国家的城乡建设用地分布与变化进行过较为深入系统的研究,但在全球尺度上,这方面研究尚为空白。本文是利用我国自主研制的世界上首套30m空间分辨率全球地表覆盖数据集GlobeLand30的人造地表数据层,首次开展了全球城乡建设用地的空间分布及变化的统计分析。它采用用地面积、构成占比和增量占比等主要指标,统计全球范围内城乡建设用地的空间分布及2000年至2010年10年间的变化,重点分析了2010年全球、各大洲及主要国家的城乡建设用地分布现状与地域差异,2000年至2010年全球、主要国家的建设用地变化以及其主要土地来源。研究结果表明,2010年全球城乡建设用地总面积为118.75×104km2,占全球陆表面积的0.88%;2000年至2010年全球城乡建设用地面积增加了5.74×104 km2,变化率为5.08%,其中,中国和美国新增城乡建设用地约占全球的一半;新增城乡建设用地占用最多的是耕地,占总量的50.26%。这些为研究全球陆表人类活动的空间分布特征与变化趋势提供了翔实的信息和知识。     

高铁轨道板快速检测方法研究

介绍目前两种轨道板检测方法并分析各方法的优缺点,提出近景摄影测量技术应用于检测轨道板的新方法。通过实验分析验证摄影测量方法检测轨道板的可行性,并从进一步提高检测轨道板效率的角度,提出需要解决的关键问题和检测方法的具体方案。     

Singular value decomposition and cluster analysis as regression diagnostics tools for geodetic applications

It is well known that high-leverage observations significantly affect the estimation of parameters. In geodetic literature, mainly redundancy numbers are used for the detection of single high-leverage observations or of single redundant observations. In this paper a further objective method for the detection of groups of important and less important (and thus redundant) observations is developed. In addition, the parameters which are predominantly affected by these groups of observations are identified. This method thus complements other diagnostics tools, such as, e.g., multiple row diagnostics methods as described in statistical literature (see, e.g., Belsley et al. in Regression diagnostics: identifying influential data and sources of collinearity. Wiley, New York, 1980). The method proposed in this paper is based on geometric aspects of adjustment theory and uses the singular value decomposition of the design matrix of an adjustment problem together with cluster analysis methods for regression diagnostics. It can be applied to any geodetic adjustment problem and can be used for the detection of (groups of) observations that significantly affect the estimated parameters or that are of negligible impact. One of the advantages of the proposed method is the improvement of the reliability of observation plans and thus the reduction of the impact of individual observations (and outliers) on the estimated parameters. This is of particular importance for the very long baseline interferometry technique which serves as an application example of the regression diagnostics tool.     

The determination of potential difference by the joint application of measured and synthetical gravity data: a case study in Hungary

In an elementary approach every geometrical height difference between the staff points of a levelling line should have a corresponding average g value for the determination of potential difference in the Earth’s gravity field. In practice this condition requires as many gravity data as the number of staff points if linear variation of g is assumed between them. Because of the expensive fieldwork, the necessary data should be supplied from different sources. This study proposes an alternative solution, which is proved at a test bed located in the Mecsek Mountains, Southwest Hungary, where a detailed gravity survey, as dense as the staff point density (~1 point/34 m), is available along a 4.3-km-long levelling line. In the first part of the paper the effect of point density of gravity data on the accuracy of potential difference is investigated. The average g value is simply derived from two neighbouring g measurements along the levelling line, which are incrementally decimated in the consecutive turns of processing. The results show that the error of the potential difference between the endpoints of the line exceeds 0.1 mm in terms of length unit if the sampling distance is greater than 2 km. Thereafter, a suitable method for the densification of the decimated g measurements is provided. It is based on forward gravity modelling utilising a high-resolution digital terrain model, the normal gravity and the complete Bouguer anomalies. The test shows that the error is only in the order of 10⁻³mm even if the sampling distance of g measurements is 4 km. As a component of the error sources of levelling, the ambiguity of the levelled height difference which is the Euclidean distance between the inclined equipotential surfaces is also investigated. Although its effect accumulated along the test line is almost zero, it reaches 0.15 mm in a 1-km-long intermediate section of the line.