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101.
基于MAPGIS的西藏羌塘地区地质构造图数字制图技术 总被引：5，自引：0，他引：5
新一轮国土资源大调查将全面引入地理信息系统和计算机技术，GIS软件为地质图件数字制图提供了现代化的技术手段，MAPGIS是国产首选GIS软件平台，具有较强的机助制图功能，自引入区调工作以来，已取得了长足的发展。西藏羌塘地区构造复杂，地质内容丰富，以该地区1：50万地质图和1：100万构造图为例，阐述了MAPGIS的数字制图技术。重点对数据准备、流程设计、矢量化、坐标网、误差校正、图幅拼接、拓扑造区、线参数编辑、色彩搭配、拷贝技术等进行详细阐述。 相似文献
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Long-term trends in the ocean wave climate because of global warming are of major concern to many stakeholders within the
maritime industries, and there is a need to take severe sea state conditions into account in design of marine structures and
in marine operations. Various stochastic models of significant wave height are reported in the literature, but most are based
on point measurements without exploiting the flexible framework of Bayesian hierarchical space–time models. This framework
allows modelling of complex dependence structures in space and time and incorporation of physical features and prior knowledge,
yet remains intuitive and easily interpreted. This paper presents a Bayesian hierarchical space–time model with a log-transform
for significant wave height data for an area in the North Atlantic ocean. The different components of the model will be outlined,
and the results from applying the model to data of different temporal resolutions will be discussed. Different model alternatives
have been tried and long-term trends in the data have been identified for all model alternatives. Overall, these trends are
in reasonable agreement and also agree fairly well with previous studies. The log-transform was included in order to account
for observed heteroscedasticity in the data, and results are compared to previous results where a similar model was employed
without a log-transform. Furthermore, a discussion of possible extensions to the model, e.g. incorporating regression terms
with relevant meteorological data, will be presented. 相似文献
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We propose a method for geometric areal object matching based on multi‐criteria decision making. To enable this method, we focused on determining the matched areal object pairs that have all relations, one‐to‐one relationships to many‐to‐many relationships, in different spatial data sets by fusing geometric criteria without user invention. First, we identified candidate corresponding areal object pairs with a graph‐based approach in training data. Second, three matching criteria (areal hausdorff distance, intersection ratio, and turning function distance) were calculated in candidate corresponding pairs and these criteria were normalized. Third, the shape similarity was calculated by weighted linear combination using the normalized matching criteria (similarities) with the criteria importance through intercriteria correlation method. Fourth, a threshold (0.738) of the shape similarity estimated in the plot of precision versus recall versus all possible thresholds of training data was applied, and the matched pairs were determined and identified. Finally, we visually validated the detection of similar areal feature pairs and conducted statistical evaluation using precision, recall, and F‐measure values from a confusion matrix. Their values were 0.905, 0.848, and 0.876, respectively. These results validate that the proposed classifier, which detects 87.6% of matched areal pairs, is highly accurate. 相似文献
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