Simulation of landscape spatial layout evolution in rural-urban fringe areas: a case study of Ganjingzi District

In recent years, the rapid expansion of urban spaces has accelerated the mutual evolution of landscape types. Analyzing and simulating spatio-temporal dynamic features of urban landscape can help to reveal its driving mechanisms and facilitate reasonable planning of urban land resources. The purpose of this study was to design a hybrid cellular automata model to simulate dynamic change in urban landscapes. The model consists of four parts: a geospatial partition, a Markov chain (MC), a multi-layer perceptron artificial neural network (MLP-ANN), and cellular automata (CA). This study employed multivariate land use data for the period 2000–2015 to conduct spatial clustering for the Ganjingzi District and to simulate landscape status evolution via a divisional composite cellular automaton model. During the period of 2000–2015, construction land and forest land areas in Ganjingzi District increased by 19.43% and 15.19%, respectively, whereas farmland, garden lands, and other land areas decreased by 43.42%, 52.14%, and 75.97%, respectively. Land use conversion potentials in different sub-regions show different characteristics in space. The overall land-change prediction accuracy for the subarea-composite model is 3% higher than that of the non-partitioned model, and misses are reduced by 3.1%. Therefore, by integrating geospatial zoning and the MLP-ANN hybrid method, the land type conversion rules of different zonings can be obtained, allowing for more effective simulations of future urban land use change. The hybrid cellular automata model developed here will provide a reference for urban planning and policy formulation.     

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.     

一种顾及道路复杂度的增量路网构建方法

针对传统路网采集和更新需要昂贵的实地测量以及大量的后续内业处理问题,提出了一种从大规模粗糙轨迹数据中自动生成路网的方法。该方法包含轨迹滤选和路网增量构建两步:第1步通过构建空间、时间、逻辑约束的规则模型,在消除数据中的噪音和冗余的同时,将原始轨迹进行合理分割,滤选形成规范轨迹集合;第2步基于信息熵计算轨迹点周围道路的复杂度,据此自动调节道路分割参数,不断将新产生的路段加入到路网,同时计算道路平均交通流量和速度等路况信息,遍历各规范轨迹的定位点重复以上处理过程,最终得到完整路网。通过昆明市200辆出租车采集的约6851万条轨迹数据进行路网构建试验,并与OpenStreetMap数据比较,证明了本文方法的有效性。与已有典型方法比较,本文方法能用更少节点提取更高质量的路网。     

小波变换和BP神经网络模型在沉降变形监测中的应用研究

变形预测在预报工程险情方面起着关键性的作用,针对施工中需及时、准确地预测变形的问题,本文利用小波变换原理对监测数据进行降噪处理,并采用BP神经网络分析不同训练样本下的预测效果和精度水平。实验结果表明:基于小波消噪后的BP网络模型,以连续的近期观测数据作为训练样本,对下期变形预测精度高,效果好,相对误差很小。因此,小波变换和BP神经网络模型在沉降变形监测工程中能作为预测研究与应用的参考。     

导航应用的按需下载区域网络模型研究

提出了一种导航应用的按需下载区域数据模型。对按需下载模式的技术要求进行了分析,提出了区域导航数据模型的定义及其算法,用UML定义了区域网络概念模型,并与ISO的网络模型进行了一致性检验。通过实际算例证明了方法的可用性。     

关于我国天测与测地VLBI网络未来建设的讨论

结合国内外VLBI技术发展的历史、现状与动态,从天体测量、空间大地测量和深空探测等角度,对我国天测与测地VLBI观测网络的未来建设进行了需求分析和测站配置技术指标分析。     

面向对象的城市管网数据组织模型设计

针对现有城市管网数据组织方式存在的问题,提出了一种基于面向对象的城市管网数据组织模型,该模型从人们认知的角度,将客观世界中的管网作为集数据和方法于一体的对象综合体,进行一体化存储和管理。模型在引入类概念的基础上,对基类中几何类和实体类的结构进行了详细设计,并讨论了模型中类与类、类与对象之间的关系,最后以城市供水管网为例,设计了基于对象的城市供水管网数据组织模型。     

港珠澳大桥首级控制网复测

介绍了港珠澳大桥首级控制网的复测方案,包括复测精度及网形设计、对控制网复测方法、技术要求及成果精度进行了介绍。最后,通过控制网复测成果和建网成果的比较,对首级控制网建网成果质量及控制点的稳定性进行了分析评价。     

南京长江三桥钢索塔拼装测量控制网的建立

南京长江三桥是我国首座主塔采用钢结构形式的特大型斜拉桥。根据钢索塔拼装测量的精度要求,介绍了该桥首级控制网的概况,对钢索塔架设专用控制网的网形选择、方案设计、观测方法和数据处理进行了探讨。实践证明,所采取的专用控制网布设方法和测量方案是合理的,对今后类似工程具有借鉴意义。     

连续运行参考站在市政工程测量中布设静态GPS控制网的应用

当使用网络RTK布设工程控制点很难达到预期的精度要求时,我们可以利用参考站高精度的坐标作为静态GPS网的起算点进行控制网布设。本文以北京某市政道路工程为例,介绍了使用这种方法布设GPS平面控制网的方法,并对控制网的精度进行分析。