海量矿业权核查成果数据存储与访问的优化技术研究

针对矿业权核查单矿权成果数据具有文件小、数量多、异构、多源等特点,为了有效地存储这些数据到Oracle数据库中,实现快速提取及显示,并能够将一个矿业权的提取时长控制在1秒以内,本文给出了从建立数据库实体到查询、显示过程所涉及诸多问题的一个完整的优化方案。首先建立了一个完整的、科学的数据存储模型,并采用范围-列表分区模式对大型表进行分区,同时采用通过调整Oracle系统高速缓冲区、减少表连接、优化SQL查询语句、在客户端分页显示查询结果等优化方案,有效提升表中数据的查询速度,以达到优化目的。     

测绘在新农村建设中存在的问题和对策

新农村建设也像城市一样，必须有测绘为其提供服务与保障。本文重点对测绘在新农村建设中存在的突出问题进行了深入分析，并就如何进一步推进新农村建设测绘工作提出了建议和对策。     

ASCOT与POS结合在IMU／DGPS辅助航空摄影中的应用

IMU/DGPS辅助航空摄影是近年来开始广泛应用的航摄新技术,该技术的使用设备十分昂贵,而且,其设备中的航空摄影管理系统不一定是必需的,本文通过改造徕卡的ASCOT系统,使其和APPLANIX公司的POS AV 510一起完成某摄区的IMU/DGPS辅助航空摄影,充分利用已有的设备,为IMU/DGPS辅助航空摄影提供了一种新的设备配置方案。     

高精度数字扫描仪的最新进展

高精度扫描是数字摄影测量生产工作流程的一个重要部分。评定性能优良扫描仪的一个重要的条件是:在一定要求下连续高速的进行高光学分辨率的胶卷扫描。目前徕卡公司在试图改善图像的质量和扫描平台控制的同时,将在DSW彩色扫描仪上提供这种快速的扫描功能。徕卡公司为了这些新的性能,已经进行了许多针对性的革新,这些新的革新技术构成了一个新的型号DSW700。     

Geodetic Height Determination in 2005 Qomolangma Survey

Determining the geodetic height of Mount Qomolangma was one of the very important missions in the 2005 Qomolangma height survey. There were three GPS networks in the survey: regional GPS crustal deformation network, geodetic GPS control network, and GPS measurement on the mountain summit. Data collection and processing were introduced. The final data processing strategy and reasonable geodetic height were fairly determined based on careful data analysis.     

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。     

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.     

Building a model-based personalised recommendation approach for tourist attractions from geotagged social media data

When travelling, people are accustomed to taking and uploading photos on social media websites, which has led to the accumulation of huge numbers of geotagged photos. Combined with multisource information (e.g. weather, transportation, or textual information), these geotagged photos could help us in constructing user preference profiles at a high level of detail. Therefore, using these geotagged photos, we built a personalised recommendation system to provide attraction recommendations that match a user's preferences. Specifically, we retrieved a geotagged photo collection from the public API for Flickr (Flickr.com) and fetched a large amount of other contextual information to rebuild a user's travel history. We then created a model-based recommendation method with a two-stage architecture that consists of candidate generation (the matching process) and candidate ranking. In the matching process, we used a support vector machine model that was modified for multiclass classification to generate the candidate list. In addition, we used a gradient boosting regression tree to score each candidate and rerank the list. Finally, we evaluated our recommendation results with respect to accuracy and ranking ability. Compared with widely used memory-based methods, our proposed method performs significantly better in the cold-start situation and when mining ‘long-tail’ data.     

无人海洋测绘技术体系构建

随着无人技术的快速发展,无人测绘的时代即将到来。本文分析了无人时代技术特征与海洋测绘发展机遇;讨论了无人海洋测绘新理念,构建了无人海洋测绘技术体系,论述了无人海洋测绘技术关键技术;展望了无人海洋测绘技术发展趋势,及其在海洋经济、海洋可持续发展中的应用前景。     

Expanding global mapping of the foliage clumping index with multi-angular POLDER three measurements: Evaluation and topographic compensation

The clumping index measures the spatial aggregation (clumped, random and regular) of foliage elements. The global mapping of the clumping index with a limited eight-month multi-angular POLDER 1 dataset is expanded by integrating new, complete year-round observations from POLDER 3. We show that terrain-induced shadows can enhance bi-directional reflectance distribution function variation and negatively bias the clumping index (i.e. indicating more vegetation clumping) in rugged terrain. Using a global high-resolution digital elevation model, a topographic compensation function is devised to correct for this terrain effect. The clumping index reductions can reach up to 30% from the topographically non-compensated values, depending on terrain complexity and land cover type. The new global clumping index map is compared with an assembled set of field measurements from 32 different sites, covering four continents and diverse biomes.