基于数据库的大比例尺基础地理数据缩编需要注意的一些问题

提出了基于数据库的大比例尺基础地理数据缩编需要注意的一些问题，并从加强数据设计的完整性、作好数据准备工作，提高自动化处理功能，人机交互辅助缩编，满足地图回放的要求，加强数据质量控制等方面阐述了实现基于数据库缩编的实用方法。     

Signal acquisition of Luojia-1A low earth orbit navigation augmentation system with software defined receiver

Low Earth Orbit (LEO) satellite navigation signal can be used as an opportunity signal in the case of a Global Navigation Satellite System (GNSS) outage, or as ...     

水电站GPS变形监测网的数据处理及稳定性分析

以2009年观测数据为例,应用GAM IT软件和CosaGPS软件对山口水电站GPS变形监测A级网的数据进行处理。探讨高精度GPS网数据处理的方法和过程,比较研究两期观测成果,分析GPS变形监测网基准点的稳定性,为山口水电站GPS变形监测提供参考。     

基于大比例尺的DLG动态维护研究

通过对大比例尺DLG更新测绘方法的调研,分析了目前社会发展对其现势性的需求,并根据现有大比例尺DLG各种不同更新技术方法,提出了一种快速、高效、低成本、高质量的动态维护机制,具有一定的应用价值。     

Normalized algorithm for mapping and dating forest disturbances and regrowth for the United States

Forest disturbances such as harvesting, wildfire and insect infestation are critical ecosystem processes affecting the carbon cycle. Because carbon dynamics are related to time since disturbance, forest stand age that can be used as a surrogate for major clear-cut/fire disturbance information has recently been recognized as an important input to forest carbon cycle models for improving prediction accuracy. In this study, forest disturbances in the USA for the period of ∼1990–2000 were mapped using 400+ pairs of re-sampled Landsat TM/ETM scenes in 500m resolution, which were provided by the Landsat Ecosystem Disturbance Adaptive Processing System project. The detected disturbances were then separated into two five-year age groups, facilitated by Forest Inventory and Analysis (FIA) data, which was used to calculate the area of forest regeneration for each county in the USA.     

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。     

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.     

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.