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.     

基于LIDAR数据的建筑物轮廓提取

建筑物轮廓的准确提取是建筑物三维重建中最重要的一步。本文在研究已有建筑物轮廓提取方法的基础上,针对LIDAR离散的点云数据,提出了一种自动快速提取建筑物轮廓信息的方法。首先通过点云数据生成城市的数字表面模型(DSM)和数字地面模型(DTM)相减计算得出规则化的数字表面模型(nDSM),进而将地面点和非地面点进行分类;其次,考虑到地物的几何特性,提出一种8邻域搜索的方法对非地面点点云进行分割,得到建筑物表面点云;最后运用基于梯度图的边界跟踪的方法来获取建筑物的轮廓信息。实验表明:该方法能有效地提取建筑物轮廓。     

导航星座抗毁组网结构研究

为提高整个二代导航卫星网络的抗毁性能,对其抗毁组网结构进行了研究。基于分群管理、链路冗余与修补方法,提出并设计分析了一种基于GEO星层、MEO星层以及地面节点联合组网的二代导航卫星网络组网结构,根据快照周期内位置固定的方法对星座中MEO星层进行分群管理,设计其群首的备份选择机制,以及群首MEO替代失效群管理者GEO机制,提高了星座网络的通信链路的抗毁性。通过分析二代导航卫星网络中数据传输过程与类型,设计网络结构中群内、星层以及整个二代导航星座的通信链路,特别是对星座中MEO星层的通信链路进行充分的冗余设计,启用视距范围内间隔失效卫星间次相邻卫星节点链路连接的链路设计,并同时提高了整个二代导航卫星网络的通信性能和通信容量,可以满足各种不同业务需求的数据包传输。     

RTK技术在沛县新城区图根控制测量中的应用

叙述了RTK技术在沛县新城区图根控制测量中的应用,通过比较分析说明：RTK的测量精度完全能够满足1：500地形测图图根控制的精度要求。     

利用GPS研究南极电离层TEC对太阳耀斑的异常响应

利用了南极地区IGS站和中山站GPS观测数据,计算了太阳耀斑期间的电离层TEC,分析了南极电离层TEC对太阳耀斑的异常响应。     

一种边缘特征增强的多源遥感影像融合方法研究

基于分频分策略的融合思想,提出了一种边缘特征增强的多源遥感影像融合方法。实验证明,融合后的影像不仅在很大程度上保留了多光谱影像的光谱特征,还提高了空间分辨率,特别是边缘特征得到进一步增强。     

不动产地籍和产权登记信息系统

介绍了一种涵盖地籍管理过程且在实施过程中简单易行而有效的信息系统,为土地资源的合理开发利用和地籍信息的动态管理了新的途径。     

Building modeling for 3D city model

New development in urban planning, cityscape, real estate management and the like, calls for new demands for 3D city model. There are so many objects in 3D city model such as building, river, road, and so on. The building model is very important in 3D city model. In recent years,a lot of research work about visualization has been done. In our opinions,visualization is only a part of 313 city model, while interactive operation about buildings is rather important as well. In order to implement interactive operation (create , edit, query,etc. ), good data structure and model must be developed.     

Enhanced orbit determination for BeiDou satellites with FengYun-3C onboard GNSS data

GPS Solutions - A key limitation for precise orbit determination of BeiDou satellites, particularly for satellites in geostationary orbit (GEO), is the relative weak geometry of ground stations....