Conference Reports

18th International Symposium of Cipa by D. M. Barber, p.505
48th Photogrammetric Week by A. T. Smart, p.506
5th Conference on Optical 3D Measurement Techniques by G. Hunter, p.509     

Book Review

Books reviewed:
S. Openshaw and R. J. Abrahart, Geocomputation
M. Craglia and H. Onsrud, Geographic Information Research: Transatlantic Perspectives
P. J. Halls, Spatial Information and the Environment
J. Sievers and T. Schneider, Second International Symposium on Geographical Names: Geonames 2000
J. C. Leachtenauer and R. G. Driggers, Surveillance and Reconnaissance Imaging Systems: Modelling and Performance Prediction
P. A. Longley, M. F. Goodchild, D. J. Maguire and D. W. Rhind, Geographic Information Systems and Science
Y. Q. Chen and Y. C. Lee, Geographical Data Acquisition
K. Borre, Plane Networks and their Applications.
A. Hirschfield and K. Bowers, Mapping and Analysing Crime Data: Lessons from Research and Practice
O. Koelbl, Proceedings of the Oeepe Workshop on Automation in Digital Photogrammetric Production
RICS Books, Vertical Aerial Photography and Derived Digital Imagery: Client Specification Guidelines
D. E. Reeve and J. R. Petch, Gis Organisations and People: A Socio-Technical Approach     

基于非线性理论的边坡稳定性评价模型

边坡稳定问题涉及到各类水利、港工、铁路和工业民用建筑工程。由于影响边坡稳定性的因素较多，而且其变形破坏机理复杂，边坡稳定性问题迄今仍然受到理论研究和工程实践的关注。本文采用非线性理论和方法来研究边坡的变形破坏机理，并建立稳定性评价模型。以分叉和突变理论引出突变级数来表征边坡的状态，并用神经网络从中获取稳定性评价和判断的知识，进而构建系统，并对各类边坡稳定状态做出分析评价。实例分析证明了该方法的有效性。     

人工神经网络在桩基工程中的应用综述

对人工神经网络在桩基工程中的应用研究工作进行了回顾与评述。总结了神经网络在单桩承载力、荷载－位移关系预测以及基桩动测完整性判释等方面的技术成果与水平，并分析和探讨了进一步的研究方向和应用前景。     

道路曲线数字化数据的联合平差模型

在讨论道路曲线基本单元数字化数据处理模型的基础上，进一步建立了道路曲线的联合平差模型，探讨了以线形为特征的交通运输空间数据的平差处理方法，提出了评价道路曲线数字化的精度指标。这些工作为研究交通空间数据的质量和建立动态分段模型以及道路GIS空间基础数据库是有益的。     

Self-organised clustering for road extraction in classified imagery

The extraction of road networks from digital imagery is a fundamental image analysis operation. Common problems encountered in automated road extraction include high sensitivity to typical scene clutter in high-resolution imagery, and inefficiency to meaningfully exploit multispectral imagery (MSI). With a ground sample distance (GSD) of less than 2 m per pixel, roads can be broadly described as elongated regions. We propose an approach of elongated region-based analysis for 2D road extraction from high-resolution imagery, which is suitable for MSI, and is insensitive to conventional edge definition. A self-organising road map (SORM) algorithm is presented, inspired from a specialised variation of Kohonen's self-organising map (SOM) neural network algorithm. A spectrally classified high-resolution image is assumed to be the input for our analysis. Our approach proceeds by performing spatial cluster analysis as a mid-level processing technique. This allows us to improve tolerance to road clutter in high-resolution images, and to minimise the effect on road extraction of common classification errors. This approach is designed in consideration of the emerging trend towards high-resolution multispectral sensors. Preliminary results demonstrate robust road extraction ability due to the non-local approach, when presented with noisy input.     

Thematic mapping from remotely sensed data with neural networks: MLP, RBF and PNN based approaches

 Neural networks are attractive tools for the derivation of thematic maps from remotely sensed data. Most attention has focused on the multilayer perceptron (MLP) network but other network types are available and have different properties that may sometimes be more appropriate for some applications. Here a MLP, radial basis function (RBF) and probabilistic neural network (PNN) were used to classify remotely sensed data of an agricultural site. The accuracy of these classifications ranged from 86.25–91.25%. The accuracy of the PNN classification could be increased through the incorporation of prior probabilities of class membership but the accuracy of each classification could also be degraded by the presence of an untrained class. Post-classification analyses, however, could be used to identify potentially misclassified cases, including those belonging to an untrained class, to increase accuracy. The effect of the post-classification analysis on the accuracy of the classification derived from each of the three network types investigated differed and it is suggested that network type be selected carefully to meet the requirements of the application in-hand. Received: 23 March 2000 / Accepted: 9 July 2000     

从遥感影像提取道路特征的方法综述与展望

从遥感影像中提取道路特征已有许多研究，但仍存在许多问题有待解决。本文探讨了道路特征提取的基本思路，对道路特征提取的基本方法和现状进行了较全面的综述，介绍了具有代表性的特征提取方法，并对各个方法的特点进行了分析，最后对道路特征提取的研究前景进行了展望。     

The development of computer networks: First results from a microeconomic model

 Computer networks like the Internet are gaining importance in social and economic life. The accelerating pace of the adoption of network technologies for business purposes is a rather recent phenomenon. Many applications are still in the early, sometimes even experimental, phase. Nevertheless, it seems to be certain that networks will change the socioeconomic structures we know today. This is the background for our special interest in the development of networks, in the role of spatial factors influencing the formation of networks, and consequences of networks on spatial structures, and in the role of externalities. This paper discusses a simple economic model – based on a microeconomic calculus – that incorporates the main factors that generate the growth of computer networks. The paper provides analytic results about the generation of computer networks. The paper discusses (1) under what conditions economic factors will initiate the process of network formation, (2) the relationship between individual and social evaluation, and (3) the efficiency of a network that is generated based on economic mechanisms. Received: 5 July 2000 / Accepted: 28 November 2000     

Forecasting Monsoon Precipitation Using Artificial Neural Networks

This paper explores the application of Artificial Intelligent (AI) techniques for climate forecast. It pres ents a study on modelling the monsoon precipitation forecast by means of Artificial Neural Networks (ANNs). Using the historical data of the total amount of summer rainfall over the Delta Area of Yangtze River in China, three ANNs models have been developed to forecast the monsoon precipitation in the corre sponding area one year, five-year, and ten-year forward respectively. Performances of the models have been validated using a ＇new＇ data set that has not been exposed to the models during the processes of model development and test. The experiment results are promising, indicating that the proposed ANNs models have good quality in terms of the accuracy, stability and generalisation ability.