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.     

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。     

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。     

平坦地区GPS高程异常拟合方法研究

结合GPS测量和水准测量资料,用BP人工神经网络和RBF人工神经网络方法和二次多项式曲面拟合方法拟合高程异常,对平坦地区GPS高程异常拟合精度进行比较分析,得出有实用价值的结论。     

Approaches for delineating landslide hazard areas using different training sites in an advanced artificial neural network model

The current paper presents landslide hazard analysis around the Cameron area, Malaysia, using advanced artificial neural networks with the help of Geographic Information System (GIS) and remote sensing techniques. Landslide locations were determined in the study area by interpretation of aerial photographs and from field investigations. Topographical and geological data as well as satellite images were collected, processed, and constructed into a spatial database using GIS and image processing. Ten factors were selected for landslide hazard including: 1) factors related to topography as slope, aspect, and curvature; 2) factors related to geology as lithology and distance from lineament; 3) factors related to drainage as distance from drainage; and 4) factors extracted from TM satellite images as land cover and the vegetation index value. An advanced artificial neural network model has been used to analyze these factors in order to establish the landslide hazard map. The back-propagation training method has been used for the selection of the five different random training sites in order to calculate the factor’s weight and then the landslide hazard indices were computed for each of the five hazard maps. Finally, the landslide hazard maps (five cases) were prepared using GIS tools. Results of the landslides hazard maps have been verified using landslide test locations that were not used during the training phase of the neural network. Our findings of verification results show an accuracy of 69%, 75%, 70%, 83% and 86% for training sites 1, 2, 3, 4 and 5 respectively. GIS data was used to efficiently analyze the large volume of data, and the artificial neural network proved to be an effective tool for landslide hazard analysis. The verification results showed sufficient agreement between the presumptive hazard map and the existing data on landslide areas.     

超高层建筑平面控制网竖向传递实践与研究

分析了超高层建筑平面控制网的建立及竖向传递的方法,介绍了数字正垂在测定超高层建筑倾斜偏差的具体应用,实现了用全站仪法和GPS法来检核控制网竖向传递的精度。     

土壤含盐量反演的研究

运用Hyperion数据,以黑龙江省大庆市某一实验区为例,开展对土壤含盐量定量提取的研究,通过对图像预处理、特征提取、建立BP神经网络模型（Back Propagation Network）等研究工作,探讨反演土壤含盐量的方法。研究结果表明：神经网络模型具有极强的线性和非线性拟合能力,模拟遥感影像特征与土壤盐分之间比较复杂的关系上有很大优势。研究结果不但为利用Hyperion数据反演土壤含盐量提供理论依据,而且还为其它地表参数的反演提供参考。