移动地质灾害应急指挥系统设计与实现

本文讨论了ArcGIS空间分析、数据库管理和移动通信等技术,并分析了地质环境因子、地质灾害危险性预报预警等级图及实际灾害点信息,将其与智能手机结合,建立了移动地质灾害应急指挥系统。通过系统随时随地获取可靠的地质灾害预警预报信息,提高对突发性地质灾害的反应能力,从而达到有效防灾、减灾的目的。     

浅谈手机GPS在国土资源管理中的实用技巧

近年来手持GPS在基层国土资源管理部门的野外调查工作中的需求越来越迫切。利用智能手机GPS的功能配合专业的手机GPS软件可快速高效地实现土地权属确认、长度测算、准确定位等野外调查和监测工作,并且能满足一般精度的测量要求。较之价格高昂的专业GPS,该方法具有费用低廉、操作便捷的特点,非常适合基层国土资源工作人员掌握使用。     

Automated road markings extraction from mobile laser scanning data

Road markings are used to provide guidance and instruction to road users for safe and comfortable driving. Enabling rapid, cost-effective and comprehensive approaches to the maintenance of route networks can be greatly improved with detailed information about location, dimension and condition of road markings. Mobile Laser Scanning (MLS) systems provide new opportunities in terms of collecting and processing this information. Laser scanning systems enable multiple attributes of the illuminated target to be recorded including intensity data. The recorded intensity data can be used to distinguish the road markings from other road surface elements due to their higher retro-reflective property. In this paper, we present an automated algorithm for extracting road markings from MLS data. We describe a robust and automated way of applying a range dependent thresholding function to the intensity values to extract road markings. We make novel use of binary morphological operations and generic knowledge of the dimensions of road markings to complete their shapes and remove other road surface elements introduced through the use of thresholding. We present a detailed analysis of the most applicable values required for the input parameters involved in our algorithm. We tested our algorithm on different road sections consisting of multiple distinct types of road markings. The successful extraction of these road markings demonstrates the effectiveness of our algorithm.     

An algorithm for automatic detection of pole-like street furniture objects from Mobile Laser Scanner point clouds

An algorithm for automatic extraction of pole-like street furniture objects using Mobile Laser Scanner data was developed and tested. The method consists in an initial simplification of the point cloud based on the regular voxelization of the space. The original point cloud is spatially discretized and a version of the point cloud whose amount of data represents 20–30% of the total is created. All the processes are carried out with the reduced version of the data, but the original point cloud is always accessible without any information loss, as each point is linked to its voxel. All the horizontal sections of the voxelized point cloud are analyzed and segmented separately. The two-dimensional fragments compatible with a section of a target pole are selected and grouped. Finally, the three-dimensional voxel representation of the detected pole-like objects is identified and the points from the original point cloud belonging to each pole-like object are extracted.The algorithm can be used with data from any Mobile Laser Scanning system, as it transforms the original point cloud and fits it into a regular grid, thus avoiding irregularities produced due to point density differences within the point cloud.The algorithm was tested in four test sites with different slopes and street shapes and features. All the target pole-like objects were detected, with the only exception of those severely occluded by large objects and some others which were either attached or too close to certain features.     

A shape-based segmentation method for mobile laser scanning point clouds

Segmentation of mobile laser point clouds of urban scenes into objects is an important step for post-processing (e.g., interpretation) of point clouds. Point clouds of urban scenes contain numerous objects with significant size variability, complex and incomplete structures, and holes or variable point densities, raising great challenges for the segmentation of mobile laser point clouds. This paper addresses these challenges by proposing a shape-based segmentation method. The proposed method first calculates the optimal neighborhood size of each point to derive the geometric features associated with it, and then classifies the point clouds according to geometric features using support vector machines (SVMs). Second, a set of rules are defined to segment the classified point clouds, and a similarity criterion for segments is proposed to overcome over-segmentation. Finally, the segmentation output is merged based on topological connectivity into a meaningful geometrical abstraction. The proposed method has been tested on point clouds of two urban scenes obtained by different mobile laser scanners. The results show that the proposed method segments large-scale mobile laser point clouds with good accuracy and computationally effective time cost, and that it segments pole-like objects particularly well.     

Urban accessibility diagnosis from mobile laser scanning data

In this paper we present an approach for automatic analysis of urban accessibility using 3D point clouds. Our approach is based on range images and it consists in two main steps: urban objects segmentation and curbs detection. Both of them are required for accessibility diagnosis and itinerary planning.Our method automatically segments facades and urban objects using two hypotheses: facades are the highest vertical structures in the scene and objects are bumps on the ground on the range image. The segmentation result is used to build an urban obstacle map. After that, the gradient is computed on the ground range image. Curb candidates are selected using height and geodesic features. Then, nearby curbs are reconnected using Bézier curves. Finally, accessibility is defined based on geometrical features and accessibility standards.Our methodology is tested on two MLS databases from Paris (France) and Enschede (The Netherlands). Our experiments show that our method has good detection rates, is fast and presents few false alarms. Our method outperforms other works reported in the literature on the same databases.     

车载激光扫描的三维道路自动提取方法

车载激光扫描系统获取的复杂道路环境点云数据量大、目标复杂,难以有效提取出道路的点云。本文通过分析扫描线上激光点云的空间分布和统计特征,提出一种适用于复杂道路环境的道路点云自动提取方法。该方法首先根据点的扫描角度或GPS时间信息提取扫描线;利用移动窗口法进行高程滤波,提取地面点云,然后采用基于路坎模型的移动窗口法提取路坎点;利用局部区域相邻扫描线的相似性特点,对提取的路坎点云进行跟踪和优化;最后利用优化后的路坎作为道路的边界实现道路路面精确提取。经过实验和分析,该方法不仅适应于有固定道路宽度的结构化道路提取,同样适用于无固定宽度的复杂道路提取。     

交流性的后结构主义路径——洛特曼符号学理论研究

洛特曼借助生物学理论,创立"文化链"等概念,树立了文本生存环境意识。在分析文本过程中设立他者地位,以多元并置的样态形成文本符号系统内外的交流互动。最终,以交流性的意识弥合了结构主义方法论中封闭性的缺陷,将他的符号学理论转向了社会文化系统,实现了对传统结构主义方法论的超越。     

2009年阿合奇M_S5.5、阿图什M_S5.0地震前后重力场变化分析

计算分析了近2期流动重力资料,对2009年4月19日阿合奇发生的MS5.5地震和2009年4月22日阿图什发生的MS5.0地震进行了初步分析。结果表明,这两次地震的孕育与发生,都伴随着重力异常的出现,且地震发生在重力场变化的零线附近和重力场反向回跳的转折线附近。     

模拟地震现场应急通信演练策划

就如何运用好目前福建省地震局所配备的地震现场应急通信系统开展演练进行了探讨。将现场通信演练细分为现场局域网系统、卫星通信系统、现场后勤保障系统和现场单兵通信系统进行策划,详细论述了演练的情景、科目、流程和演练目标等方面的内容。地震现场应急通信的演练策划所涉及的演练设备、演练内容、演练策略、演练评价及演练追踪评价等方面也做了相应的阐述。