Accurately estimating the deformation of high-rise building is a very important work for surveyors, however it is very difficult to get an accurate and reliable predictor. In this paper, artificial neural network has been applied here because of its good ability of nonlinear fitting. On the basis of the high-rise building monitoring data, three prediction models including the BP, RBF and GRNN neural network prediction models were established, the comparative analysis for the prediction accuracy of the three models was obtained. The results show that neural network is capable for prediction, and GRNN possess higher capability in prediction and better adaptability in comparing with other two neural networks.
Land Use/Cover Change (LUCC) is a commonly concerned issue. The CLUE-S model was applied to Yangzhou urban area in this paper to simulate the land use spatial distribution in the urban area from 2003 to 2010. Combined with RS & GIS technology, three periods of remote sensing images were firstly preprocessed and three periods of land-use maps were obtained by means of object-oriented method. Then, corresponding model parameters were defined in the CLUE-S model to obtain the spatial distribution of land use of Yangzhou urban in 2003~2010. After that, the extracted and the simulated land use maps in 2007 were compared to evaluate the simulation accuracy. CLUE-S model can be used to simulate the distribution pattern of the development of smaller-scale regional urban space, to provide guidance for the smaller scale urban development planning, and is worthy of popularization and application of land use and land cover change model.
For recent decades, especially with the launch of international commercial satellites carrying high-precision sensors, land use dynamic monitoring techniques based on high-resolution remote sensing imagery have undergone a phase of rapid development of key techniques applied in many aspects vastly. This paper is focused on the technological research and procedure of these key techniques, including the process of multi-temporal images, the information classification and feature extraction method, and also the analysis of classified result and driving factors influencing mutual conversion between land classes. Considering the representative meaning as its typicality in China, Yangzhou urban area was regarded as the study zone with multi-temporal images of three periods in the year of 1988, 2002 and 2007. During the procedure of classifying, object-oriented method was applied to e36xtract features from different multi-temporal imagery. Through comparative analysis, it was demonstrated that residential area expanded rapidly especially from 2002 to 2007 and area of the arable and other kinds of land cover reduced considerably. Finally, it is concluded that multiple driving forces affected the land use/change and multivariable linear regression model was used to explore the primary and secondary forces.
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