Promoting Local Growth: Process,Practice, and Policy     

《The Professional geographer》2013,65(1):108-109

Electromap World Atlas, Version 1.1.

Interactive Population Statistical System, Version 1.0. Jerry W. Wicks and Jose Luiz Pereira de Almeida

MATHCAD, Version 2.5.

Memory Mate.

Micro DEM+, Version 5.21. Peter Guth

NCSS – Number Cruncher Statistical System, Version 5.02. Dr. Jerry L. Hintze  相似文献   

Predicting potential distributions of geographic events using one-class data: concepts and methods     

Q. Guo  W. Li  Y. Liu  D. Tong 《International journal of geographical information science》2013,27(10):1697-1715

One common problem with geographic data is that, for a specific geographic event, only occurrence information is available; information about the absence of the event is not available. We refer to these specific types of geospatial data as geographic one-class data (GOCD). Predicting the potential spatial distributions that a particular geographic event may occur from GOCD is difficult because traditional binary classification methods that require availability of both positive and negative training samples cannot be used. The objective of this research is to define GOCD and propose novel approaches for modelling potential spatial distributions of geographic events using GOCD. We investigate the effectiveness of one-class support vector machine (OCSVM), maximum entropy (MAXENT) and the newly proposed positive and unlabelled learning (PUL) algorithm for solving GOCD problems using a case study: species distribution modelling from synthetic data. Our experimental results indicate that generally OCSVM, MAXENT and PUL are effective in modelling the GOCD. Each method has advantages and disadvantages, but PUL seems to be the most promising method.  相似文献   

Mining urban land-use patterns from volunteered geographic information by means of genetic algorithms and artificial neural networks     

Julian Hagenauer  Marco Helbich 《International journal of geographical information science》2013,27(6):963-982

In the context of OpenStreetMap (OSM), spatial data quality, in particular completeness, is an essential aspect of its fitness for use in specific applications, such as planning tasks. To mitigate the effect of completeness errors in OSM, this study proposes a methodological framework for predicting by means of OSM urban areas in Europe that are currently not mapped or only partially mapped. For this purpose, a machine learning approach consisting of artificial neural networks and genetic algorithms is applied. Under the premise of existing OSM data, the model estimates missing urban areas with an overall squared correlation coefficient (R ²) of 0.589. Interregional comparisons of European regions confirm spatial heterogeneity in the model performance, whereas the R ² ranges from 0.129 up to 0.789. These results show that the delineation of urban areas by means of the presented methodology depends strongly on location.  相似文献   

Integration of Geospatial Science in Teacher Education     

《The Journal of geography》2012,111(5):163-172

Abstract

One of the primary missions of our university is to train future primary and secondary teachers. Geospatial sciences, including GIS, have long been excluded from teacher education curriculum. This article explains the curriculum revisions undertaken to increase the geospatial technology education of future teachers. A general education class introducing geospatial technology to the general student body has been developed, a cartography class has been modified to provide applied geospatial experience explicitly for future teachers, and a service learning partnership with local K–12 schools has been established where students are working with teachers to integrate geospatial sciences in their academic programs.  相似文献   

Development and future prospects of quantitative mineral assessment in China     

《China Geology》2019,2(2):198-210

Mineral potential assessment at the Earth’s surface has been an important research for geoscientists around the world in the past five decades. The fundamental aspects of mineral assessment at different scales can be associated with the following tasks, e.g., mineral potential mapping and estimation of mineral resources. This paper summarized the history and development in terms of theories, methods technologies and software platforms for quantitative assessment of mineral resources in China, e.g. comprehensive information methodology, geological anomaly, three-component quantitative prediction method, 5P ore-finding area, integrated information assessment method, nonlinear process modeling and fractals, three dimensional mineral potential mapping, etc. At last, to discuss the future of quantitative mineral assessment in an era of big data including platform for 3D visualization, analysis and sharing, new methods and protocols for data cleaning, information enhancement, information integration, and uncertainties and multiple explanations of multi-information.© 2019 China Geology Editorial Office.  相似文献   

基于改进InceptionV3模型的常见矿物智能识别     

彭伟航  白林  商世为  唐小洁  张哲远 《地质通报》2019,38(12):2059-2066

以常见的16类矿物作为研究对象,收集每一类矿物约1000张图像,按比例划分为训练集、验证集和测试集,通过图像随机选取增加数据的多样性,建立矿物识别InceptionV3模型,训练7万次在测试集上获得81%的识别正确率。通过对损失函数的改进,引入Center Loss损失函数,训练40万次识别准确率提高到86%。对分类的混淆矩阵分析发现,孔雀石等外观特征明显的矿物识别正确率很高,而闪锌矿等与其他矿物容易混淆导致正确率较低。从特征图分析看出,模型很好地提取了孔雀石的放射状特征,矿物图像特征向量聚集程度很高,也说明了模型的可靠性。  相似文献   

基于非监督学习的水力压裂裂缝平面识别     

马孜卓  郑忆康  薛清峰  翟鸿宇  雷兴林 《中国地震》2021,37(2):322-331

在水力压裂施工中,如何有效获取压裂过程中产生的裂缝形态以及裂缝的动态扩展过程一直是困扰学术界和工业界的问题。目前,常规利用微地震事件定位结果进行分析的方法存在需要人工干预、散点信息表示能力不足等问题;采用数值模拟分析的方法往往因复杂的地下介质情况而引入计算偏差。本文基于非监督学习算法,通过提取微地震事件的空间和时间信息,实现对裂缝平面的识别以及裂缝网络拓展路径的分析;并通过引入水力压裂岩石物理实验,利用实际监测获得的声发射数据以及对应的真实破裂情况的CT扫描数据,检验方法的可行性。最终结果表明,本文所提方法对主断裂有较好的识别效果,识别结果与CT扫描的真实结果吻合性较好。  相似文献   

Recent advances in earthquake monitoring II: Emergence of next-generation intelligent systems          下载免费PDF全文

Zefeng Li 《地震科学（英文版）》2021,34(6):531-540

Seismic data processing techniques, together with seismic instrumentation, determine our earthquake monitoring capability and the quality of resulting earthquake catalogs. This paper is intended to review the improvement of earthquake monitoring capability from the perspective of data processing. Over the past two decades, seismologists have made considerable advancements in seismic data processing, partly thanks to the significant development of computational power, signal processing, and machine learning techniques. In particular, wide application of template matching and increasing use of deep learning significantly enhance our capability to extract signals of small earthquakes from noisy data. Relative location techniques provide a critical tool to elucidate fault geometries and seismicity migration patterns at unprecedented resolution. These techniques are becoming standard, leading to emerging intelligent software systems for next-generation earthquake monitoring. Prospective improvements in future research must consider the urgent needs in highly generalizable detection algorithms (for both permanent and temporary deployments) and in emergency real-time monitoring of ongoing sequences (e.g., aftershock and induced seismicity sequences). We believe that the maturing of intelligent and high-resolution processing systems could transform traditional earthquake monitoring workflows and eventually liberate seismologists from laborious catalog construction tasks.  相似文献   

Ensemble prediction model of solar proton events associated with solar flares and coronal mass ejections     

Xin Huang  Hua-Ning Wang  Le-Ping Li 《天体物理学报》2012,(3):313-321

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一种利用流形学习进行多影像匹配的方法     

李爱霞  关泽群  冯甜甜 《武汉大学学报(信息科学版)》2012,37(11)

提出了一种利用流形学习方法进行多影像匹配的算法。通过两组宽基线序列影像的匹配实验,并与LE-SIFT方法、SVD-SIFT方法和基于局部线性映射的LLE-SVD方法进行了对比分析。结果表明,本文方法在获得多幅影像的共同匹配结果和利用多幅影像之间的约束实现两幅影像匹配的结果上都优于现有方法。  相似文献