Internet teaching foundation for the Remote Sensing Core Curriculum program     

T.W. Foresman  T. Cary  T. Shupin  R. Eastman  J.E. Estes  N. Faust  J.R. Jensen  K.K. Kemp 《ISPRS Journal of Photogrammetry and Remote Sensing》1997,52(6):294-300

The Remote Sensing Core Curriculum (RSCC) was initiated in 1993 to meet the demands for a college-level set of resources to enhance the quality of education across national and international campuses. The American Society of Photogrammetry and Remote Sensing adopted the RSCC in 1996 to sustain support of this educational initiative for its membership and collegiate community. A series of volumes, containing lectures, exercises, and data, is being created by expert contributors to address the different technical fields of remote sensing. The RSCC program is designed to operate on the Internet taking full advantage of the World Wide Web (WWW) technology for distance learning. The issues of curriculum development related to the educational setting, with demands on faculty, students, and facilities, is considered to understand the new paradigms for WW-influenced computer-aided learning. The WWW is shown to be especially appropriate for facilitating remote sensing education with requirements for addressing image data sets and multimedia learning tools. The RSCC is located at http://www.umbc.edu/rscc  相似文献   

一种新型自动开槽机及其控制     

靳建刚  王丽红 《中国煤田地质》2004,16(5):60-62

针对棒、管料开槽的问题，提出了一种新型全自动开槽机。介绍了该开槽机的结构和工作原理，建立全自动开槽机相适应的液压系统，搭建了开槽机继电器——控制器控制电路，实现了该设备的全自动过程控制。  相似文献   

Web Use in Geographical and Environmental Education: An International Survey at the Primary and Secondary Level   总被引：2，自引：0，他引：2  

Lea Houtsonen  Ilta-Kanerva Kankaanrinta  Antti Rehunen 《GeoJournal》2004,60(2):165-174

This article aims to study Web use and Web-based co-operation and collaboration in geographical and environmental education at the primary and secondary level around the world. Recent trends and future opportunities and challenges are taken into account. The theoretical part of the study considers Web use and different forms of Web-based co-operation. Web use and co-operation in education are classified as co-operative learning, collaborative learning or communal learning. Web use in geographical and environmental education is noted to be growing in significance. Web-based co-operation at any level of intensity is associated with many opportunities and challenges. The empirical part of this study involves a survey of geographical and environmental education researchers in various countries about their views of Web use in education. The results of this survey indicate that the Web in general finds minimal use in geographical and environmental education. As access to the Web is limited and only some pupils can use it, co-operation, particularly collaborative learning on the Web, is still rare in geographical and environmental education. The most often used application is e-mail. Researchers recognise the potential of the Web to enhance local, national and international co-operation, and to facilitate a better understanding of geographical and environmental issues at the grass-root level. Web-based learning can also help to increase and deepen the pupils' cultural understanding. Before that, however, problems in access, costs and teacher training must be solved. This revised version was published online in August 2006 with corrections to the Cover Date.  相似文献   

The Effect of Machine Behaviour and Mechanical Properties of Intact Sandstone Under Static and Dynamic Uniaxial Cyclic Loading   总被引：5，自引：0，他引：5  

M. N. Bagde  V. Petroš 《Rock Mechanics and Rock Engineering》2005,38(1):59-67

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Simulation of site-scale water fluxes in desert and natural oasis ecosystems of the arid region in Northwest China     

Mingjuan Xie  Geping Luo  Olaf Hellwich  Amaury Frankl  Wenqiang Zhang  Chunbo Chen  Chen Zhang  Philippe De Maeyer 《水文研究》2021,35(12):e14444

The study of water fluxes is important to better understand hydrological cycles in arid regions. Data-driven machine learning models have been recently applied to water flux simulation. Previous studies have built site-scale simulation models of water fluxes for individual sites separately, requiring a large amount of data from each site and significant computation time. For arid areas, there is no consensus as to the optimal model and variable selection method to simulate water fluxes. Using data from seven flux observation sites in the arid region of Northwest China, this study compared the performance of random forest (RF), support vector machine (SVM), back propagation neural network (BPNN), and multiple linear regression (MLR) models in simulating water fluxes. Additionally, the study investigated inter-annual and seasonal variation in water fluxes and the dominant drivers of this variation at different sites. A universal simulation model for water flux was built using the RF approach and key variables as determined by MLR, incorporating data from all sites. Model performance of the SVM algorithm (R² = 0.25–0.90) was slightly worse than that of the RF algorithm (R² = 0.41–0.91); the BPNN algorithm performed poorly in most cases (R² = 0.15–0.88). Similarly, the MLR results were limited and unreliable (R² = 0.00–0.66). Using the universal RF model, annual water fluxes were found to be much higher than the precipitation received at each site, and natural oases showed higher fluxes than desert ecosystems. Water fluxes were highest during the growing season (May–September) and lowest during the non-growing season (October–April). Furthermore, the dominant drivers of water flux variation were various among different sites, but the normalized difference vegetation index (NDVI), soil moisture and soil temperature were important at most sites. This study provides useful insights for simulating water fluxes in desert and oasis ecosystems, understanding patterns of variation and the underlying mechanisms. Besides, these results can make a contribution as the decision-making basis to the water management in desert and oasis ecosystems.  相似文献   

Site characterization model using least‐square support vector machine and relevance vector machine based on corrected SPT data (Nc)     

Pijush Samui  T. G. Sitharam 《国际地质力学数值与分析法杂志》2010,34(7):755-770

Statistical learning algorithms provide a viable framework for geotechnical engineering modeling. This paper describes two statistical learning algorithms applied for site characterization modeling based on standard penetration test (SPT) data. More than 2700 field SPT values (N) have been collected from 766 boreholes spread over an area of 220 sqkm area in Bangalore. To get N corrected value (N_c), N values have been corrected (N_c) for different parameters such as overburden stress, size of borehole, type of sampler, length of connecting rod, etc. In three‐dimensional site characterization model, the function N_c=N_c (X, Y, Z), where X, Y and Z are the coordinates of a point corresponding to N_c value, is to be approximated in which N_c value at any half‐space point in Bangalore can be determined. The first algorithm uses least‐square support vector machine (LSSVM), which is related to a ridge regression type of support vector machine. The second algorithm uses relevance vector machine (RVM), which combines the strengths of kernel‐based methods and Bayesian theory to establish the relationships between a set of input vectors and a desired output. The paper also presents the comparative study between the developed LSSVM and RVM model for site characterization. Copyright © 2009 John Wiley & Sons, Ltd.  相似文献   

高分一号遥感影像地质灾害信息提取方法研究   总被引：1，自引：0，他引：1  

李启源  王明常  王凤艳  谭洋  卢立吉 《测绘与空间地理信息》2016,(2):17-20

山区地质灾害发生频繁,受灾范围大,危害严重。提取地质灾害信息、估算受灾情况,对救灾工作极为重要。传统的遥感人工解译方法速度慢、效率低;计算机自动解译速度快但解译精度受影像质量影响大,大范围地区很难建立起普适性的解译模型。本文利用洮南市西部高分一号遥感数据,结合DEM生成三维影像,建立解译标志,解译基础地质信息,分析地质灾害成因;根据研究区泥石流地质灾害的主要影像特征,提取影像的分类属性,基于面向对象的分类方法,建立信息提取模型,快速提取出地质灾害敏感区域;再进行人机交互,精确提取出地质灾害的类型和范围,通过野外验证,该方法十分可靠,为大范围地质灾害信息快速提取和灾后救援提供科学依据。  相似文献   

迁移学习支持下的高分影像山地滑坡灾害解译模型     

郭加伟  李永树  李政  刘锟铭  张帅毅 《测绘科学技术学报》2016,(5):496-501

无人机低空遥感是近年来新兴的一种快速获取灾情信息的手段,如何利用无人机高分影像构建滑坡灾害解译模型是实现快速自动解译滑坡的关键。针对该问题,对比了多种影像特征提取方法,将迁移学习(TL)特征和支持向量机(SVM)引入到构建滑坡灾害自动解译模型中,提出了一种TL支持下的高分影像滑坡灾害解译模型。选取5·12汶川地震及4·20芦山地震系列无人机影像构建了滑坡灾害样本库并进行了实验,TL特征方法整体分类准确度ACC为95%,ROC达到0.98,识别准确率达到97%。结果表明,所提方法可用于高分影像滑坡自动解译,同时可用于大面积高分影像中快速山地滑坡灾害定位及检测。  相似文献   

高光谱影像概率分类向量机分类方法研究     

薛志祥  余旭初  张鹏强  谭熊  魏祥坡 《测绘科学技术学报》2016,(4):426-430

从分析基于支持向量机和相关向量机的高光谱影像分类方法的优势和不足出发,将基于概率分类向量机的方法用于高光谱影像分类试验。在贝叶斯理论框架下,概率分类向量机为基函数权值引入截断Gauss先验概率分布,使得不同类别的基函数权值具有不同符号的先验分布,并利用EM算法进行参数推断,得到足够稀疏的概率模型,弥补了相关向量机选取错误类别的样本作为相关向量的不足,从而有效地提高了模型的分类精度和稳定性。OMIS和PHI影像分类试验表明,概率分类向量机能够很好地应用在高光谱影像分类。  相似文献   

豫西地区雷暴预警预报系统构建方法研究     

李振锋  付伟基  陈红霞 《气象与环境科学》2016,39(4):126-134

目前,基层台站在雷暴预警预报中主要依赖传统经验预报方法,而对卫星云图、大气电场、多普勒雷达回波和高分辨率数值预报产品等资料释用水平不高,存在主观定性分析多、客观定量计算少等不足。依托现有气象资料和雷暴预报方法,以雷暴预警预报系统构建为目标,从气象数据仓库构建、支持向量机雷暴预报、相似预报雷暴预报、Poor Man集合预报技术和多源数据融合分析等方面,探讨了雷暴预警预报系统构建方法,实现了雷暴中期预测、短期预报和短时临近预警三个层次的预报功能,解决了气象资料庞杂难以管理、数值预报产品释用水平不高、数值预报结果存在"跃变"、多源数据融合分析缺乏手段等问题,提高了基层台站雷暴预报工作效率和预报水平,同时也为其他地区类似系统构建提供了参考。  相似文献