机器自监督学习的建筑物面要素几何形状度量     

马磊  闫浩文  王中辉  刘波  吕文清 《测绘科学》2017,(12):171-177

针对传统方法在度量建筑物面要素几何形状时,未能考虑形状认知的视觉特征因素且形状特征需要人为定义等问题,该文提出一种建筑物几何形状度量方法。首先,利用深度卷积神经网络的图像特征学习特性,结合自动编码机的自监督学习能力,构建基于机器自监督学习的建筑物面要素几何形状度量神经网络;其次,利用建筑物图像形状大数据对网络进行训练;最后,利用训练完成的神经网络识别并提取建筑物形状特征集并作为形状度量的结果。实验表明,该方法形状度量结果区分度高,一定程度上克服了人为定义形状特征的缺点,且与视觉感知结果基本一致。  相似文献   

遥感影像分类的归类学习方法     

孙立新  罗高平 《测绘工程》1998,7(3):39-43,49

遥感影像分类专家系统是遥感分类研究中的一个重要发展方向,然而,传统的统计模式识别法和人工神经网络分类法除了能完成具体的影像分类外,不能提供易于被人类理解的分类知识,文中介绍一种基于扩张矩阵的示例学习方法,并将其应用于遥感影像分类知识的自动获取。  相似文献   

Seafloor sediment classification from single beam echo sounder data using LVQ network     

Y. Satyanarayana  Sanjeev Naithani  R. Anu 《Marine Geophysical Researches》2007,28(2):95-99

Seafloor sediment classification based on echo characteristics obtained from single-beam echosounder is very useful in remote and instant sediment classification. Results of different classification techniques using such data provide robust results when the acoustic beam has a normal incidence with the seabottom. This may not always be true and show poor classification, with the data acquired during rough sea periods corresponding to both oblique and normal incidence of the acoustic pulse, due to roll and pitch motion of the ship. In the present study, an attempt is made to exploit the artificial neural network (ANN) techniques for better classification with such data. Learning Vector Quantisation (LVQ) is a supervised learning algorithm of ANN that is found to be an effective tool and show good performance. The input data to the network include the roughness index (E1) and hardness index (E2) derived from echo characteristics. The network utilizes the competitive learning, a distance function in the first layer and a linear function in the second layer. The network was tried with a different size of hidden neurons and training data size to see the influence on classification. It is found that with ten neurons in the first layer and four neurons in the second layer good performance in classification for the data was achieved.  相似文献   

基于机器学习的参考作物蒸散量估算研究   总被引：2，自引：0，他引：2  

毛亚萍  房世峰 《地球信息科学学报》2020,22(8):1692-1701

参考作物蒸散量（Reference Evapotranspiration, ET₀）的准确估算对区域水资源管理和分配、流域水量平衡以及气候变化等研究具有重要作用。新疆地处我国西北干旱地区,水资源供需矛盾尖锐,精确估算该地区的ET₀有助于其科学合理地调配水资源,缓解水资源供需压力。FAO推荐的Penman-Monteith法是计算ET₀的标准方法,但该方法需要多项气象因子,而新疆地区气象站点较少且分布不均,精确完备的气象数据在新疆大部分区域难以获取。因此,如何使用有限气象因子获取高精度的ET₀在新疆地区备受关注。本文基于中国气象数据网提供的新疆地区1980—2019年的地面气候资料日值数据集,在日和月尺度下,通过对最高气温T_max、最低气温T_min、平均气温T_avg、风速U₂、相对湿度RH和日照时数n共6项气象因子进行敏感性分析,形成不同的气象因子组合;然后使用SVM、RF、GBDT和ELM 4种机器学习算法,以FAO-56 PM计算值为标准值,对新疆地区的ET₀进行了拟合建模;最后,从拟合精度、稳定性和计算代价3个方面对模型进行评价。研究表明：① 在新疆地区,ET₀对RH、T_max和U₂敏感系数级别为高,平均敏感系数分别为-0.516、0.283和0.266;n为中等,平均敏感系数为0.124;T_min和T_avg为低,平均敏感系数分别为-0.016和-0.003;② 在日尺度,各算法在RH、T_max、U₂和n这4项气象因子为输入时精度较高（RMSE<0.5 mm/day,R²>0.95）,可对ET₀进行精确估算;在月尺度,各算法使用RH、T_max和U₂这3项参数便可对ET₀进行精确估算。SVM和GBDT这2种算法在日尺度和月尺度都有较好的适用性,可在相应尺度下使用较少气象因子替代FAO-56 PM公式对ET₀进行估算。  相似文献   

Understanding uncertainty and reducing vulnerability: lessons from resilience thinking   总被引：3，自引：4，他引：3  

Fikret Berkes 《Natural Hazards》2007,41(2):283-295

Vulnerability is registered not by exposure to hazards alone; it also resides in the resilience of the system experiencing the hazard. Resilience (the capacity of a system to absorb recurrent disturbances, such as natural disasters, so as to retain essential structures, processes and feedbacks) is important for the discussion of vulnerability for three reasons: (1) it helps evaluate hazards holistically in coupled human–environment systems, (2) it puts the emphasis on the ability of a system to deal with a hazard, absorbing the disturbance or adapting to it, and (3) it is forward-looking and helps explore policy options for dealing with uncertainty and future change. Building resilience into human–environment systems is an effective way to cope with change characterized by surprises and unknowable risks. There seem to be four clusters of factors relevant to building resilience: (1) learning to live with change and uncertainty, (2) nurturing various types of ecological, social and political diversity for increasing options and reducing risks, (3) increasing the range of knowledge for learning and problem-solving, and (4) creating opportunities for␣self-organization, including strengthening of local institutions and building cross-scale linkages and problem-solving networks.  相似文献   

区域发展理论:回顾与展望   总被引：33，自引：2，他引：31  

苗长虹 《地理科学进展》1999,18(4):296-305

区域发展理论是经济地理学和区域科学基础理论的核心。本文首先回顾了传统的区域发展理论, 包括发展阶段理论、均衡增长理论、不均衡增长理论、区域增长的一般理论模式和新马克思主义理论, 然后概述了70 年代以来区域发展理论研究的新进展, 最后前瞻了区域发展理论研究与发展的基本方向。  相似文献   

智能矿产地质调查方法——以甘肃大桥-崖湾地区为例     

杨明莉  薛林福  冉祥金  桑学佳  燕群  戴均豪 《岩石学报》2021,37(12):3880-3892

在矿产地质调查理论与实践的基础上,提出一种智能矿产地质调查方法,指出智能矿产地质调查生态系统是与智能矿产地质调查相关的智能数据采集设备、应用、用户、标准、规范、智能地质调查云平台等组成部分及相互关系构成的完整系统。智能矿产地质调查的主要步骤包括：智能数据分析、重点工作区圈定、矿产地质数据采集、重点区野外工作、智能找矿预测等。提出了数据驱动与知识驱动相结合的找矿预测方法,集成了采用深度学习技术进行特征匹配找矿预测的方法和基于知识图谱的找矿预测方法。设计和基本实现了智能矿产地质调查云平台的架构与功能。应用特征匹配找矿预测方法在甘肃大桥-崖湾地区圈定了5个找矿预测区。  相似文献   

Linear and non-linear approaches to predict the Darcy-Weisbach friction factor of overland flow using the extreme learning machine approach     

Kiyoumars Roushangar  Saba Mirza Alipour  Dominique Mouaze 《国际泥沙研究》2018,33(4):415-432

In modeling of overland flow and erosion, the overland flow friction factor (f), is a crucial factor. Due to the importance of a good understanding of f and its variability, the current study aimed to investigate the capability of non-linear approaches to estimate the Darcy-Weisbach friction factor of overland flow and its components (sediment transport, wave, form, and grain friction factors) through the Extreme Learning Machine (ELM) approach. Four datasets were used herein which were obtained from flume experiments done by different researchers. In order to investigate the effects of different parameters on the friction factor, numerous models consisting of various parameters were utilized to predict the friction factor using the ELM approach. The modeling procedure was established in two stages; the first stage aimed to model the overland flow friction factor and investigate the effect of the different parameters on the friction factor using non-linear separation via the ELM approach. In the second stage, the friction factor was linearly separated into different types of friction factors and then the separate components were estimated. Sensitivity analysis results confirmed the key role of Froude number (Fr) values for most of the models. On the other hand, the results obtained for estimated values of the friction factor were acceptable and outperformed available empirical approaches.  相似文献   

Application of Machine Learning Methods in Arrival Time Picking of P Waves from Reservoir Earthquakes     

HU Jiupeng  YU Ziye  KUANG Wenhuan  WANG Weitao  RUAN Xiang  DAI Shigui 《中国地震研究》2020,34(3):343-357

Reservoir earthquake characteristics such as small magnitude and large quantity may result in low monitoring efficiency when using traditional methods. However, methods based on deep learning can discriminate the seismic phases of small earthquakes in a reservoir and ensure rapid processing of arrival time picking. The present study establishes a deep learning network model combining a convolutional neural network (CNN) and recurrent neural network (RNN). The neural network training uses the waveforms of 60 000 small earthquakes within a magnitude range of 0.8-1.2 recorded by 73 stations near the Dagangshan Reservoir in Sichuan Province as well as the data of the manually picked P-wave arrival time. The neural network automatically picks the P-wave arrival time, providing a strong constraint for small earthquake positioning. The model is shown to achieve an accuracy rate of 90.7% in picking P waves of microseisms in the reservoir area, with a recall rate reaching 92.6% and an error rate lower than 2%. The results indicate that the relevant network structure has high accuracy for picking the P-wave arrival times of small earthquakes, thus providing new technical measures for subsequent microseismic monitoring in the reservoir area.  相似文献   

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雷雨  赵丹宁 《大地测量与地球动力学》2013,33(5):53-57

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