21世纪逐日无缝数据立方体构建方法及逐年逐季节土地覆盖和土地利用动态制图—中国智慧遥感制图iMap （China） 1.0

粮食安全、高质量人居环境建设、生物多样性保护、星球健康等社会可持续发展目标和对地球系统的理解、模拟与管理都迫切需要多尺度、长时序、辐射和几何精度高且一致性强的遥感观测数据集和针对用户需求的、信息主题灵活的制图产品。但是由于技术和成本限制,传统的遥感卫星难以提供同时具有高空间分辨率、高时间频率和高质量的观测数据。现有的制图和反演方案多是针对于单一传感器系列,难以充分挖掘和联合利用多源异构遥感大数据的信息潜力,造成观测时段和分辨率有限、时空一致性和可比性较差。因此,遥感领域迫切需要新的技术范式。本文基于前沿的云计算、人工智能、虚拟星座、时空融合重建等技术,针对现有遥感大数据特别是国产卫星数据,提出一套智慧遥感制图(iMap)框架。该框架从用户需求出发、问题驱动,能够大大改善当前遥感数据产品难以满足农林管理、国情监测、生态环境保护、防灾减灾、城市建设等用户的多样化、高精度地表监测需求的现状。在该框架的指导下,基于亚马逊云计算(AWS)高性能、高弹性、可扩展的分布式计算资源,搭建了在线实时、自动化、无服务器、端到端的遥感大数据生产链和并行制图系统,并生产了首套21世纪中国全境逐日无缝数据立方体(SDC)及逐年逐季节土地覆盖和土地利用制图产品。逐日SDC综合利用Landsat和MODIS卫星数据构建虚拟星座,并通过多源时空数据融合重建技术研制得到无云无缝、高精度的反射率产品,作为分析就绪数据(ARD),为高精度定量遥感反演和制图打下根基。基于这一SDC,完成了逐年逐季节地表制图。逐年平均精度超过80%。在制图过程中,基于多套多层土地覆盖和土地利用分类体系,运用有限样本稳定分类理论,迁移使用全季节普适样本库,采用自动机器学习(AutoML)策略集成优化多种分类器,并结合时空一致性变化检测和后处理技术。这两套制图产品证明了本文提出的智慧遥感制图框架的可行性和有效性。未来将进一步完善和发展该框架,以开放和灵活的理念,为促进中国遥感进一步发展提供新的思路。     

地震相分析技术在滨里海盆地AS油田沉积相分析中的应用

针对滨里海AS地区的勘探开发目标层系,运用人工神经网络和主组分分析等技术和方法,对研究区的地震反射波形变化特征及其反映的地质特征进行识别和分类,得到地震相图,应用主要地震相单元中单井相分析成果建立地震相与沉积相的对应关系,将地震相转化为沉积相.该方法弥补了单纯用钻井资料进行沉积相划分的缺陷,提高了沉积相划分精度.     

EXPERIMENTS ON THE GROUP GENERALIZATIONG OF CONTOUR LINES FOR TOPOGRAPHIC MAPS

ABSTRACT

This paper analyzes the advantages and disadvantages of two technical lines for automatic group generalization of contour lines. The author suggests that it is possible to get faster and better generalization results if we simulate the intelligence of human experts in program designing, retrieve geomorphological structural information using the input data of 2-D contour lines and derive and output the generalized 2-D results directly.     

人行为计算机仿真的一种方法

根据人工神经网络的基本原理和模拟人类大脑思维的过程,提出了一种神经网络模型和思维模型系统,用于人行为的计算机仿真.神经网络模型采用Sigmoid函数.思维模型系统由感知系统、推理机、知识库、存储单元、学习训练系统、决策系统以及行为输出系统7个部分组成.采用此系统模型对普通学生的工作日日常行为进行了计算机仿真实验.实验结果达到了预期的满意效果.     

Tunnel stability analysis during construction using a neuro‐fuzzy system

This paper presents an alternative strategy to evaluate the stability of tunnels during the design and construction stages based on a hybrid system, composed by neural, neuro‐fuzzy and analytical solutions. A prototype of this system is designed using a database formed by 261 cases, 45 real and the rest synthetic. This system is capable of reproducing the displacements induced at the periphery of the tunnel before and after support installation. The stability of the excavation process is evaluated using a criterion that considers dimensionless parameters based on the shear strength of the media, the induced deformation level in the ground, the plastic radii and the advance of excavation without support. The efficiency and validity of the prototype is verified with two examples of actual tunnels, one included in the database used to train the system and the other not included. The results of both examples show a better approximation than other commonly used techniques. Copyright © 2005 John Wiley & Sons, Ltd.     

Image and non-image parameters of atmospheric Cherenkov events: a comparative study of their γ-ray/hadron classification potential in ultrahigh energy regime

In this exploratory simulation study, we compare the event-progenitor classification potential of a variety of measurable parameters of atmospheric Cherenkov pulses which are produced by ultrahigh energy γ-ray and hadron progenitors and are likely to be recorded by the TACTIC (TeV atmospheric Cherenkov telescope with imaging camera) array of atmospheric Cherenkov telescopes. The parameters derived from Cherenkov images include Hillas, fractal and wavelet moments, while those obtained from non-image Cherenkov data consist of pulse profile rise time and base width and the relative ultraviolet to visible light content of the Cherenkov event. It is shown by a neural-net approach that these parameters, when used in suitable combinations, can bring about a proper segregation of the two event types, even with modest sized data samples of progenitor particles.     

基于CAD技术的地下管线规划辅助决策平台实现

地下管线是城市基础设施的重要组成部分,是城市规划、建设、管理的重要基础。本文基于CAD技术,介绍了地下管线规划辅助决策平台的技术路线、关键技术和功能实现,实现管线规划审批的全过程管理。     

Neural networks and landslide susceptibility: a case study of the urban area of Potenza

For those working in the field of landslide prevention, the estimation of hazard levels and the consequent production of thematic maps are principal objectives. They are achieved through careful analytical studies of the characteristics of landslide prone areas, thus, providing useful information regarding possible future phenomena. Such maps represent a fundamental step in the drawing up of adequate measures of landslide hazard mitigation. However, for a complete estimation of landslide hazard, meant as the degree of probability that a landslide occurs in a given area, within a given space of time, detailed and uniformly distributed data regarding their incidence and causes are required. This information, while obtainable through laborious historical research, is usually partial, incomplete and uneven, and hence, unsatisfactory for zoning on a regional scale. In order to carry this out effectively, the utilization of spatial estimation of the relative levels of landslide hazard in the various areas was considered opportune. These areas were classified according to their levels of proneness to landslide activity without taking recurrence periods into account. Various techniques were developed in order to obtain upheaval numerical estimates. The method used in this study, which was applied in the area of Potenza, is based on techniques derived from artificial intelligence (Artificial Neural Network—ANN). This method requires the definition of appropriate thematic layers, which parameterize the area under study. These are recognized by means of specific analyses in a functional relationship to the event itself. The parameters adopted are: slope gradient, slope aspect, topographical index, topographical shape, elevation, land use and lithology.     

Response of the interaction between groundwater and other components of the environment in Mexico

This article reviews and discusses environmental aspects related to vertical upward and downward groundwater flow. Flow systems are an important tool to understand groundwater functioning as related to the environment, in terms of obtaining indicators of human impact and solving specific questions about a groundwater-environment system that has been influenced by anthropogenic means. This involves two broad processes. First, groundwater changes due to activities of man in the surrounding environment as a result of: (1) alteration of recharge by modification of native vegetation and original soil cover; (2) reduction of groundwater discharge to coastal areas and to inland water bodies producing desiccation of wetlands, lakes and springs; (3) groundwater contamination from sewage looses and uncontrolled waste disposal locations, and (4) up-welling of undesirable water quality induced by extraction. Second, environmental alterations due to changes in the groundwater regime produce: (1) increase in soil erosion through the disappearance of vegetation due to water-table decline, (2) water-table rise due to unplanned artificial recharge resulting from water imports to a catchment, (3) decline in water levels for improper extraction regime, (4) soil subsidence due to extraction mismanagement, and (5) disappearance of phreatofites caused by excessive extraction. Unless further understanding between groundwater and the other components of the environment is sought, the relationship between people and its environment will be subject to some of these effects, potentially endangering adequate human development and sustainable water management.     

An application of artificial intelligence for rainfall-runoff modeling

This study proposes an application of two techniques of artificial intelligence (AI) for rainfall-runoff modeling: the artificial neural networks (ANN) and the evolutionary computation (EC). Two different ANN techniques, the feed forward back propagation (FFBP) and generalized regression neural network (GRNN) methods are compared with one EC method, Gene Expression Programming (GEP) which is a new evolutionary algorithm that evolves computer programs. The daily hydrometeorological data of three rainfall stations and one streamflow station for Juniata River Basin in Pennsylvania state of USA are taken into consideration in the model development. Statistical parameters such as average, standard deviation, coefficient of variation, skewness, minimum and maximum values, as well as criteria such as mean square error (MSE) and determination coefficient (R ²) are used to measure the performance of the models. The results indicate that the proposed genetic programming (GP) formulation performs quite well compared to results obtained by ANNs and is quite practical for use. It is concluded from the results that GEP can be proposed as an alternative to ANN models.