虚拟参考站技术（VRS）在城市控制网应用中的精度分析

介绍了VRS技术的概念、工作原理、技术方法以及影响VRS测量精度的几大关键因素，通过对试验数据大量的比测与研究，将影响测量精度的因素进行了深入的探讨与分析。     

无棱镜技术在电厂施工土方量测量中的应用

随着全站仪在测量中的广泛应用,测量的精度有了很大的提高.特别是无棱镜技术的发展,改变了以往的测量手段和方法,提高了工作的效率.土方量测量是施工过程中不可避免的,为此本文介绍无棱镜测量技术在洛河电厂施工过程中测量土方量的应用情况,通过对比实验,我们对无棱镜测量技术工作效率和成果精度以及适用条件进行分析.     

Automatic interpretation of digital maps

In the past, the availability and/or the acquisition of spatial data were often the main problems of the realization of spatial applications. Meanwhile this situation has changed: on one hand, comprehensive spatial datasets already exist and on the other hand, new sensor technologies have the ability to capture fast and with high quality large amounts of spatial data. More and more responsible for the increasing accessibility of spatial data are also collaborative mapping techniques which enable users to create maps by themselves and to make them available in the internet. However, the potential of this diversity of spatial data can only hardly be utilized. Especially maps in the internet are represented very often only with graphical elements and no explicit information about the map’s scale, extension and content is available. Nevertheless, humans are able to extract this information and to interpret maps. For example, it is possible for a human to distinguish between rural and industrial areas only by looking at the objects’ geometries. Furthermore, a human can easily identify and group map objects that belong together. Also the type, scale and extension of a map can be identified under certain conditions only by looking at the objects’ geometries. All these examples can be subsumed under the term “map interpretation”. In this paper it is discussed how map interpretation can be automated and how automatic map interpretation can be used in order to support other processes. The different kinds of automatic map interpretation are discussed and two approaches are shown in detail.     

全球导航卫星系统在民用航空中的应用

分析了民用航空应用对全球导航卫星系统性能的严格要求。介绍了GPS系统对其精度、完好性等性能进行增强的措施,及在民用航空中的应用,进一步介绍了未来多卫星导航系统为满足航空应用需求而做的努力。介绍了卫星导航系统目前在我国民用航空中的应用情况,强调了未来卫星导航系统对我国航空应用的重要意义。     

C++Builder结合Matlab在测量数据处理中的应用

根据测量数据处理的特点,通过对C++Builder6.0和MATLAB特点的分析,得出C++Builder6.0和MATLAB语言两者结合,能够大大提高测量数据处理效率。详细介绍了一种以C++Builder为系统开发平台,利用MATLAB进行测量数据处理的过程。并列举平差程序的一部分源码供参考。     

基于MapGIS的气象预报分析系统的设计与实现

介绍了以MapGIS地理信息系统为技术平台所开发的气象预报分析系统(WeatherGIS)的组织结构,数据录入、来源数据编辑、数据处理、结果数据编辑、数据输出、系统设置等模块的主要功能,以及系统应用状况。     

从数字化测绘到信息化测绘的测绘学科新进展

测绘学科已完成由传统模拟测绘向数字化测绘的转变过程,现在正在向信息化测绘新阶段转化与跨越。本文综述了测绘学科及其下的五个子学科(大地测量学、摄影测量与遥感学、地图制图与地理信息工程学、工程测量学和海洋测绘学)在向信息化测绘跨越过程中的科学与技术的新进展。     

机载LiDAR技术

机载LiDAR集成了GPS、IMU、激光扫描仪新一代航空遥感系统,能利用飞行获取的原始数据直接生成DEM和正射影像图,而无需任何或仅需少量的地面控制点。与传统的摄影测量技术相比,机载LiDAR可以节省大量的人力、时间和经费。在过去十年,机载LiDAR作为精确、快速的地球表面三维测量方法已得到广泛的认同,本文详细介绍了机载LiDAR的基本原理、技术结构及数据处理流程。     

Design of high precision horizontal control network for large-scale hydropower project

IntroductionThe design of high precision horizontal controlnetwork for large-scale hydropower project toconstruct control network and measure deforma-tion of monitoring network.It can be also divid-ed into horizontal control network and verticalcontrol ne…     

Semantic segmentation of high spatial resolution images with deep neural networks

Availability of reliable delineation of urban lands is fundamental to applications such as infrastructure management and urban planning. An accurate semantic segmentation approach can assign each pixel of remotely sensed imagery a reliable ground object class. In this paper, we propose an end-to-end deep learning architecture to perform the pixel-level understanding of high spatial resolution remote sensing images. Both local and global contextual information are considered. The local contexts are learned by the deep residual net, and the multi-scale global contexts are extracted by a pyramid pooling module. These contextual features are concatenated to predict labels for each pixel. In addition, multiple additional losses are proposed to enhance our deep learning network to optimize multi-level features from different resolution images simultaneously. Two public datasets, including Vaihingen and Potsdam datasets, are used to assess the performance of the proposed deep neural network. Comparison with the results from the published state-of-the-art algorithms demonstrates the effectiveness of our approach.