基于Google Earth的天然气加气站管理系统的设计与实现

Google Earth(GE)作为一款数据开放式的卫星影像浏览软件,已被应用到消防、农业发展、公共安全等重要领域中,并发挥关键性作用。文中提出一套基于Google Earth组件的开发方法,通过面向对象的程序开发技术,设计并实现天然气加气站信息管理系统,且具备一定的空间分析能力。以昆明市主城区内天然气加气站信息为例,验证本系统的可行性和稳定性,证明Google Earth平台的功能实用性。     

基于物联网与WebGIS的智能公交系统

通过对物联网技术与WebGIS技术特点的分析,阐述了物联网与WebGIS之间的关系。运用WebGIS对空间信息的强大处理能力与物联网物物相连的特性,采集交通环境中的城市公交车各类信息收集至WebGIS平台进行存储与分析,并利用Google map api实现城市公交信息的可视化表达与各传感器的查询定位,为WebGIS与物联网的综合应用研究提供了新的思路。     

基于Google Earth的GIS数据管理系统的设计与实现

Google Earth作为公共地图服务平台,为测绘、勘探等工作提供了数据支持。本文探讨了开发应用软件,将GIS数据批量转换为Google Earth数据的关键技术,实现了数据管理、数据转换等功能。     

基于深度神经网络的蒙古国色楞格河流域水体信息提取

蒙古高原地处干旱半干旱地区,河流水系对该区域的资源环境格局及其生态环境影响重大。发源于蒙古国的色楞格河是蒙古高原最主要的水资源来源,准确掌握该流域的水体信息对东北亚地区生态环境问题及资源保护具有重要意义。本文以蒙古高原色楞格河流域为研究对象,基于谷歌地球引擎（Google Earth Engine,GEE）云平台,使用 Sentinel-2 多光谱卫星遥感影像,利用深度神经网络（Deep Neural Network, DNN）方法对色楞格河流域的水体信息进行提取,并与支持向量机方法进行对比;利用全球30 m SRTM数据生成水系分布矢量图,通过空间分析形成河流提取目标区,结合深度神经网络分类结果,绘制蒙古国色楞格河流域2019年河流分布图。研究结果表明：① 该方法能够准确地完成大流域范围内的水体制图,提取结果能够体现色楞格河流域河流的空间分布,且能够减少河流断流、空洞现象;② 深度神经网络模型中批量大小设置为8时,在处理数据速度与精度中达到最优,而神经网络结构中隐含层数达到4层时,在精度评价指标测试数据集上达到0.9666,保证了模型特征挖掘能力;③ 经样本点的验证,结果总体精度达到97.65%,可以满足实际应用需求。本研究预期可以为蒙古高原的水体提取提供方法支持和相关数据支持。     

基于WKML库的Google Earth二次开发及其在测绘中的应用

Goolge Earth具有很强大的显示、查询和分析功能,而直接使用KML对Google Earth进行二次开发,通常难以实现数据的批量化处理。因此,介绍一种简单的方法对Google Earth进行二次开发,并将这种方法引入到测绘领域中,以实现测量数据的可视化、查询与分析。     

基于Google Map API的校园电子地图开发

Google Map API为用户提供向Google地图添加各种个性化内容的功能,用户在此基础上进行二次开发,可实现各类网络地图服务。利用Google Map API开发校园电子地图,实现电子地图的各项基本功能,并探讨功能实现中的关键部分设计。     

Google Earth在野外初步断裂调查中的应用

主要介绍了卫星影像技术及Google Earth的特点,通过工程实例说明了Google Earth作为卫星影像在野外断裂调查中发挥的作用,总结了Google Earth卫星影像的优劣。     

IGRF国际地磁参考场模型可视化研究

IGRF（International Geomagnetic Reference Field）即全球地磁参考模型,是由国际地磁和高空物理学联合会（International Association of Geomagnetism and Aeronomy）发布的一系列关于地球主磁场及其年变率的数学模型。作为科学研究和工程应用的背景场、参考场广泛用于地球深部、地壳、电离层和磁层的研究。本文以最新的一代至第10代IGRF模型为基础,以Google公司开发的虚拟三维地球软件Google Earth为载体,进行了国际地磁参考场可视化研究。生成了一系列地磁要素的KML文件。并发布在世界数据中心中国地球物理学科中心的网站上,用户通过下载并打开这些文件就可以在Google Earth上查看到中国地区地磁场7个分量的可视化结果。本文所使用的方法也同样适用于第一代至第九代IGRF模型以及今后将要建立的IGRF模型。     

Multi‐scale relief model (MSRM): a new algorithm for the visualization of subtle topographic change of variable size in digital elevation models

Morphological analysis of landforms has traditionally relied on the interpretation of imagery. Although imagery provides a natural view of an area of interest (AOI) images are largely hindered by the environmental conditions at the time of image acquisition, the quality of the image and, mainly, the lack of topographical information, which is an essential factor for a correct understanding of the AOI's geomorphology. More recently digital surface models (DSMs) have been incorporated into the analytical toolbox of geomorphologists. These are usually high‐resolution models derived from digital photogrammetric processes or LiDAR data. However, these are restricted to relatively small areas and are expensive or complex to acquire, which limits widespread implementation. In this paper, we present the multi‐scale relief model (MSRM), which is a new algorithm for the visual interpretation of landforms using DSMs. The significance of this new method lies in its capacity to extract landform morphology from both high‐ and low‐resolution DSMs independently of the shape or scale of the landform under study. This method thus provides important advantages compared to previous approaches as it: (1) allows the use of worldwide medium resolution models, such as SRTM, ASTER GDEM, ALOS, and TanDEM‐X; (2) offers an alternative to traditional photograph interpretation that does not rely on the quality of the imagery employed nor on the environmental conditions and time of its acquisition; and (3) can be easily implemented for large areas using traditional GIS/RS software. The algorithm is tested in the Sutlej‐Yamuna interfluve, which is a very large low‐relief alluvial plain in northwest India where 10 000 km of palaeoriver channels have been mapped using MSRM. The code, written in Google Earth Engine's implementation of JavaScript, is provided as Supporting Information for its use in any other AOI without particular technical knowledge or access to topographical data. © 2017 The Authors. Earth Surface Processes and Landforms published by John Wiley & Sons Ltd.     

Spatial Distribution of Seismic Landslides in the Areas of 1927 Gulang M8.0 Earthquake

The 1927 Gulang M8.0 earthquake has triggered a huge number of landslides, resulting in massive loss of people''s life and property. However, integrated investigations and results regarding the landslides triggered by this earthquake are rare; such situation hinders the deep understanding of these landslides such as scale, extent, and distribution. With the support of Google Earth software, this study intends to finish the seismic landslides interpretation work in the areas of Gulang earthquake (VIII-XI degree) using the artificial visual interpretation method, and further analyze the spatial distribution and impact factors of these landslides. The results show that the earthquake has triggered at least 936 landslides in the VIII-XI degree zone, with a total landslide area of 58.6 km². The dense area of seismic landslides is located in the middle and southern parts of the X intensity circle. Statistical analysis shows that seismic landslides is mainly controlled by factors such as elevation, slope gradient, slope direction, strata, seismic intensity, faults and rivers. The elevation of 2 000-2 800 m is the high-incidence interval of the landslide. The landslide density is larger with a higher slope gradient. East and west directions are the dominant sliding directions. The areas with Cretaceous and Quaternary strata are the main areas of the Gulang seismic landslides. The X intensity zone triggered the most landslides. In addition, landslides often occur in regions near rivers and faults. This paper provides a scientific reference for exploring the development regularities of landslides triggered by the 1927 Gulang earthquake and effectively mitigating the landslide disasters of the earthquake.