A new insight into land use classification based on aggregated mobile phone data

Land-use classification is essential for urban planning. Urban land-use types can be differentiated either by their physical characteristics (such as reflectivity and texture) or social functions. Remote sensing techniques have been recognized as a vital method for urban land-use classification because of their ability to capture the physical characteristics of land use. Although significant progress has been achieved in remote sensing methods designed for urban land-use classification, most techniques focus on physical characteristics, whereas knowledge of social functions is not adequately used. Owing to the wide usage of mobile phones, the activities of residents, which can be retrieved from the mobile phone data, can be determined in order to indicate the social function of land use. This could bring about the opportunity to derive land-use information from mobile phone data. To verify the application of this new data source to urban land-use classification, we first construct a vector of aggregated mobile phone data to characterize land-use types. This vector is composed of two aspects: the normalized hourly call volume and the total call volume. A semi-supervised fuzzy c-means clustering approach is then applied to infer the land-use types. The method is validated using mobile phone data collected in Singapore. Land use is determined with a detection rate of 58.03%. An analysis of the land-use classification results shows that the detection rate decreases as the heterogeneity of land use increases, and increases as the density of cell phone towers increases.     

基于遗传小波神经网络的海底声学底质识别分类

分割海底声纳探测图像,提取单元特征向量进行主成份分析,选取均值、标准差、对比度、相关系数、能量及同质性作为训练特征向量,构建小波神经网络。利用遗传算法优化小波神经网络的初始权值及小波参数,对砂、礁石、泥3种底质类型分别进行训练,并得到3种底质的测试精度都在90%以上,优于单独利用小波神经网络进行训练时的测试精度,克服了小波神经网络训练时易陷入局部极小的固有缺陷,表明基于遗传算法的小波神经网络可有效用于海底底质声纳图像的识别和分类。     

Assessing integration of intensity,polarimetric scattering,interferometric coherence and spatial texture metrics in PALSAR-derived land cover classification

Synthetic aperture radar (SAR) is an important alternative to optical remote sensing due to its ability to acquire data regardless of weather conditions and day/night cycle. The Phased Array type L-band SAR (PALSAR) onboard the Advanced Land Observing Satellite (ALOS) provided new opportunities for vegetation and land cover mapping. Most previous studies employing PALSAR investigated the use of one or two feature types (e.g. intensity, coherence); however, little effort has been devoted to assessing the simultaneous integration of multiple types of features. In this study, we bridged this gap by evaluating the potential of using numerous metrics expressing four feature types: intensity, polarimetric scattering, interferometric coherence and spatial texture. Our case study was conducted in Central New York State, USA using multitemporal PALSAR imagery from 2010. The land cover classification implemented an ensemble learning algorithm, namely random forest. Accuracies of each classified map produced from different combinations of features were assessed on a pixel-by-pixel basis using validation data obtained from a stratified random sample. Among the different combinations of feature types evaluated, intensity was the most indispensable because intensity was included in all of the highest accuracy scenarios. However, relative to using only intensity metrics, combining all four feature types increased overall accuracy by 7%. Producer’s and user’s accuracies of the four vegetation classes improved considerably for the best performing combination of features when compared to classifications using only a single feature type.     

Multi-class geospatial object detection and geographic image classification based on collection of part detectors

The rapid development of remote sensing technology has facilitated us the acquisition of remote sensing images with higher and higher spatial resolution, but how to automatically understand the image contents is still a big challenge. In this paper, we develop a practical and rotation-invariant framework for multi-class geospatial object detection and geographic image classification based on collection of part detectors (COPD). The COPD is composed of a set of representative and discriminative part detectors, where each part detector is a linear support vector machine (SVM) classifier used for the detection of objects or recurring spatial patterns within a certain range of orientation. Specifically, when performing multi-class geospatial object detection, we learn a set of seed-based part detectors where each part detector corresponds to a particular viewpoint of an object class, so the collection of them provides a solution for rotation-invariant detection of multi-class objects. When performing geographic image classification, we utilize a large number of pre-trained part detectors to discovery distinctive visual parts from images and use them as attributes to represent the images. Comprehensive evaluations on two remote sensing image databases and comparisons with some state-of-the-art approaches demonstrate the effectiveness and superiority of the developed framework.     

Content Variations of Rubidium and Bromide in Sylvite Derived from Carnallite by Leaching of Magnesium Chloride

正The relationship between sylvinite and carnallite is important in the potash mine body contained carnallite,especially sylvinite overlies carnallite,the reverse of a normal depositional sequence.Trace elements are more     

地理国情（市情）普查外业信息采集

地理国情监测不仅扩大了测绘行业的内涵,更重要的是,对我国总体布局和规划提供了可靠的依据。目前,在全国范围内逐步开展地理国情普查工作,我分院按照安排,以厦门市为试点,对其进行了外业的调查与核查工作。结合实地作业情况来看,可以在地理国情普查内容与指标基本不变的前提下,依据地域的不同增加分类或修改部分类别的内涵。为今后全面普查工作的顺利开展,对现有的作业方式和作业内容都应有一定的规范。     

重庆万州区山地分类定量化研究

文章在GIS技术支持下,基于水文分析模型,通过DEM数据,结合山地综合分类模型,以重庆市万州区为例,对山地进行定量化分类研究,结果表明:以负地形洼地贡献区域作为山地相对高程的起算面,完全摆脱了人为因素的影响,解决了一直以来难以确定山地相对高度起算范围的难题;以负地形洼地深度作为山地相对高程是正确可行的,且能快速获取山地的相对高程;以山地表面积代替投影面积计算山地率更切合实际.     

ADINA有限元软件中材料本构的二次开发

美国ADINA R&D公司开发的软件产品ADINA经过20多年的商业开发,目前已成为全球最重要的非线性求解软件,尤其对结构非线性、流-固耦合等复杂问题的求解具有强大优势,被认为是非线性有限元发展方向的先导。然而,任何商业化的有限元软件都不可能满足客户的所有要求,例如,ADINA软件中没有岩土工程领域常用的Duncan-Chang模型。以土工数值分析中广泛应用的Duncan-Chang E-B模型为例,介绍在ADINA软件中进行材料本构二次开发的方法,给出了开发过程,通过对3个常规三轴压缩模型的数值模拟验证程序的可靠性。结果表明,基于ADINA开发的材料本构模型具有计算速度快、精度高和前后处理快捷方便的优点。另外,验证结果也表明该软件具有超强的非线性求解能力和可靠的求解结果。     

语义因子支持的多源POI分类信息一致化处理

为了解决网络POI分类异构导致多源POI数据匹配与整合困难的问题,该文利用分类特征词提取、特征词去重与优化等关键技术,构建POI分类的语义概念格,然后通过概念格之间的语义关联关系,实现多源异构POI分类体系的映射与转换。最后通过实验验证了该文方法的有效性,并将其成功应用于省级地理信息公共服务平台(政务版)数据更新。     

关于金伯利岩分类命名的建议

国内岩石工作者对金伯利岩的分类命名不尽统一,阻碍了各金伯利岩产区的相互交流和对比。本文在分析国内现有多个分类方案的基础上,汲取其合理部分,并结合代表性产地的实际资料,提出关于对我国内生金伯利岩按“构造-结构-矿物(金云母)含量”定量分类命名的表格。