Economic Change in Africa

Abstract

The Geographer at Work by Peter Gould Boston: Routledge &; Kegan Paul, 1985. x and 351 pp., figs., photos, maps, and index. $39.95 cloth; $16.95 paper.

Recollections of a Revolution: Geography as Spatial Science by Mark Billinge, Derek Gregory and Ron Martin, eds. New York: St. Martin's Press, 1984. viii and 235 pp., figs., tables, refs., and index. $25.00 cloth.

The Role of Place in Literature by Leonard Lutwack Syracuse, NY: Syracuse University Press, 1984. viii and 274 pp., refs., and index. $24.95 cloth.

The Essential Landscape: The New Mexico Photographic Survey with Essays by J.B. Jackson. by Steven A. Yates, ed. New Mexico: The University of New Mexico Press, 1985. vii and 147 pp., and photos. $45.00 cloth.

Latin America: Case Studies by Richard G. Boehm and Sent Visser, eds. NCGE Pacesetter Series. Dubuque, IA: Kendall/Hunt, 1984. x and 300 pp., figs., tables, photos, maps, apps., refs., and index. $24.95 paper.     

基于统计学习方法的安徽省安庆市自然资源自动化监测——以山体为例

遥感作为一种可以快速、大范围获取地表覆盖信息的技术手段,为复杂的自然资源调查任务提供了可靠的数据来源。针对山体确界问题,以遥感卫星影像为数据支撑,采用非监督的统计学习方法,为山体特征建模。然后,采用DBSCAN算法和边缘检测思想,识别山体区域,并提取山体边界。该方法不依赖于人工标记真值,实现了山体边界的全自动识别。实验采用安庆市Landsat 8遥感卫星影像数据,有效识别了安庆市境内的山体,并提取山体边界。通过定性和定量化分析,验证了方法的可靠性,证明了遥感技术和统计学习理论在自然资源调查领域的应用潜力。该研究方法和结果能够为安庆市明确山体范围,界定山体的完整性与山体保护规划工作提供理论支撑。     

2022年1月8日青海门源6.9级地震机器学习地震预警震级估计与现地阈值报警的回溯验证

2022年1月8日1时45分青海省海北州门源县发生6.9级地震, 周边地区普遍有感, 并导致多条高铁线路临时停运. 本文利用这次地震获取的大量烈度计加速度记录, 基于正在进行系统研发的机器学习地震预警方法模块, 对地震预警震级估计与现地阈值报警进行了回溯验证. 结果表明: 在地震发生后3.1 s, 震级估计为6.5级, 且震级估计误差不受信噪比和震中距变化的影响, 随着首台触发后时间的增加, 震级估计逐渐接近实际震级. 对于现地地震动速度峰值PGV(Peak Ground Velocity)预测, 各个台站在P波到达后3 s时, 预测PGV与观测PGV呈现1:1线性关系, 随着P波到达后时间窗的增加, 预测PGV逐步接近观测PGV, 且PGV预测误差不受信噪比和震中距变化的影响. 现地台站仪器烈度阈值设置为Ⅵ度时, 报警成功、误报、漏报的百分比分别为99.53%、0%、0.47%, 平均预警时间为19.62 s, 且地震烈度Ⅵ度区内没有发生误报和漏报; 现地台站仪器烈度阈值设置为Ⅶ度时, 报警成功、误报、漏报的百分比分别为99.77%、0%、0.23%, 平均预警时间为9.69 s, 且地震烈度Ⅶ度区内没有发生误报和漏报. 此次回溯验证结果表明: 机器学习方法在这次地震中可以得到鲁棒的震级估计和现地阈值报警结果, 并为该方法的在线测试以及中国地震预警系统升级提供可行性依据; 其次, 在这次地震事件中, 烈度计可为预警提供额外的作用, 这也为烈度计在未来地震预警的研究和应用中提供了更多的可能性.

     

深度学习PaddlePaddle框架支持下的遥感智能视觉平台研究与实现

百度深度学习PaddlePaddle框架支持下的遥感智能视觉平台,能够运用深度学习技术实现遥感影像的智能建模、训练和解译。本文通过深入分析PaddlePaddle图像分割模型库PaddleSeg的图像处理深度学习算法模型DeepLabV3+、U²-Net及RetinaNet,开发设计了遥感智能视觉平台,实现了遥感影像的地块分割、变化检测和斜框检测等专业功能。研究表明：遥感智能视觉平台提取的图斑总面积是目视解译的80%、有效图斑比例为76%、错误图斑比例为18%,实现了快速有效的遥感图像智能处理。     

面向高分遥感影像道路提取的轻量级双注意力和特征补偿残差网络模型

针对高分辨率遥感影像背景复杂,道路提取容易受阴影、建筑物和铁路等背景信息干扰的问题,提出一种带有轻量级双注意力和特征补偿机制的DAFCResUnet模型。该模型在ResUnet的基础上,通过增加轻量级的双注意力和特征补偿模块实现模型在性能和时空复杂度上的平衡。其中,双注意力模块可以增强模型的特征提取能力,特征补偿模块可以融合网络中来自深浅层的道路特征。在DeepGlobe和GF-2道路数据集上的实验结果表明,DAFCResUnet模型的IoU和F1-score可以达到0.6713、0.8033和0.7402、0.8507,模型的整体精度优于U-Net、ResUnet和VNet模型。与U-Net和ResUnet模型相比,DAFCResUnet模型仅增加了少量的计算量和参数量,但IoU和F1-score均有较大幅度的提高;与VNet模型相比,DAFCResUnet模型在计算量和参数量远低于VNet的情况下取得了更高的精度,模型在精度和时空复杂度两方面均有优势。相比其他对比模型,DAFCResUnet模型具有更强的特征提取和抗干扰能力,能更好解决道路上的干扰物、与道路特征相似地物、树荫或阴影...     

Prediction of Stellar Atmospheric Parameters using Instance-Based Machine Learning and Genetic Algorithms

In this article we present a method for the automated prediction of stellar atmospheric parameters from spectral indices. This method uses a genetic algorithm (GA) for the selection of relevant spectral indices and prototypical stars and predicts their properties, using the k-nearest neighbors method (KNN). We have applied the method to predict the effective temperature, surface gravity, metallicity, luminosity class and spectral class of stars from spectral indices. Our experimental results show that the feature selection performed by the genetic algorithm reduces the running time of KNN up to 92%, and the predictive accuracy error up to 35%. This revised version was published online in July 2006 with corrections to the Cover Date.     

A novel hybrid approach of Bayesian Logistic Regression and its ensembles for landslide susceptibility assessment

Abstract

A novel artificial intelligence approach of Bayesian Logistic Regression (BLR) and its ensembles [Random Subspace (RS), Adaboost (AB), Multiboost (MB) and Bagging] was introduced for landslide susceptibility mapping in a part of Kamyaran city in Kurdistan Province, Iran. A spatial database was generated which includes a total of 60 landslide locations and a set of conditioning factors tested by the Information Gain Ratio technique. Performance of these models was evaluated using the area under the ROC curve (AUROC) and statistical index-based methods. Results showed that the hybrid ensemble models could significantly improve the performance of the base classifier of BLR (AUROC?=?0.930). However, RS model (AUROC?=?0.975) had the highest performance in comparison to other landslide ensemble models, followed by Bagging (AUROC?=?0.972), MB (AUROC?=?0.970) and AB (AUROC?=?0.957) models, respectively.     

递进式项目教学法在“GIS程序设计”课程中的探索与实践

"GIS程序设计"作为地理信息科学专业的必修课,对学生专业能力的培养起到重要作用,同时GIS开发也是目前地理信息科学专业就业的主要方向。文中结合工程实践,通过校园地理信息系统、森林火灾扩散模拟分析与决策系统、小组答辩自选专题地理信息系统3个从简单到复杂的GIS项目,运用递进式的项目教学方法和团队协作、课上实践、课上提问、课内外答疑、课后作业、编写项目文档等措施对该课程进行改革探索和实践,并取得较好的教学效果。     

成人英语学习动机行为调查与教学对策研究

动机是激励人们行动的心理动因.英语学习动机是推动英语学习的内部动因,成人英语学习动机是复杂的.了解他们的英语学习动机特点有利于教师改进教学手段和策略,激发与培养其学习的主动性和积极性,发挥其学习潜能,从而提高英语学习的效果.运用定量研究的方法,在年轻干部学员中进行学习动机调查,分析了其学习动机的特点,并提出成人英语教学的对策.     

Effects of misclassification costs on mapping mineral prospectivity

Mineral prospectivity mapping is a classification process because in a given study area, a specific region is classified as either a prospective or non-prospective area. The cost of false negative errors differs from the cost of false positive errors because false positive errors lead to wasting much more financial and material resources, whereas false negative errors result in the loss of mineral deposits. Traditional machine learning algorithms using for mapping mineral prospectivity are aimed to minimize classification errors and ignore the cost-sensitive effects. In this study, the effects of misclassification costs on mapping mineral prospectivity are explored. The cost-sensitive neural network (CSNN) for minimizing misclassification costs is applied to map Fe polymetallic prospectivity in China’s southwestern Fujian metalorganic belt (SFMB). A CSNN with a different cost ratio ranging from 1:10 to 10:1 was used to represent various misclassification costs. The cross-validation results indicated a lower misclassification cost compared to traditional neural networks through a threshold-moving based CSNN. The CSNN’s predictive results were compared to those of a traditional neural network, and the results demonstrate that the CSNN method is useful for mapping mineral prospectivity. The targets can be used to further explore undiscovered deposits in the study area.