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Pattern recognition in road networks can be used for different applications, including spatiotemporal data mining, automated map generalization, data matching of different levels of detail, and other important research topics. Grid patterns are a common pattern type. This paper proposes and implements a method for grid pattern recognition based on the idea of mesh classification through a supervised learning process. To train the classifier, training datasets are selected from worldwide city samples with different cultural, historical, and geographical environments. Meshes are subsequently labeled as composing or noncomposing grids by participants in an experiment, and the mesh measures are defined while accounting for the mesh’s individual characteristics and spatial context. The classifier is generated using the C4.5 algorithm. The accuracy of the classifier is evaluated using Kappa statistics and the overall rate of correctness. The average Kappa value is approximately 0.74, which corresponds to a total accuracy of 87.5%. Additionally, the rationality of the classifier is evaluated in an interpretation step. Two other existing grid pattern recognition methods were also tested on the datasets, and comparison results indicate that our approach is effective in identifying grid patterns in road networks. 相似文献
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Galen J. Maclaurin 《地理信息系统科学与遥感》2016,53(6):759-777
Regional and national level land cover datasets, such as the National Land Cover Database (NLCD) in the United States, have become an important resource in physical and social science research. Updates to the NLCD have been conducted every 5 years since 2001; however, the procedure for producing a new release is labor-intensive and time-consuming, taking 3 or 4 years to complete. Furthermore, in most countries very few, if any, such releases exist, and thus there is high demand for efficient production of land cover data at different points in time. In this paper, an active machine learning framework for temporal updating (or backcasting) of land cover data is proposed and tested for three study sites covered by the NLCD. The approach employs a maximum entropy classifier to extract information from one Landsat image using the NLCD, and then replicate the classification on a Landsat image for the same geographic extent from a different point in time to create land cover data of similar quality. Results show that this framework can effectively replicate the land cover database in the temporal domain with similar levels of overall and within class agreement when compared against high resolution reference land cover datasets. These results demonstrate that the land cover information encapsulated in the NLCD can effectively be extracted using solely Landsat imagery for replication purposes. The algorithm is fully automated and scalable for applications at landscape and regional scales for multiple points in time. 相似文献
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Jo Beth Mullens 《The Journal of geography》2016,115(6):244-255
Charging undergraduate geography students with the task of designing a recreational trail in their local community offers an engaging experiential opportunity with potential to advance geographic learning in a real-world setting. This article presents an assignment in which students were asked to develop a recreational trail proposal for an undeveloped local conservation area and the results of a survey that asked the students to reflect and report upon the educational value of this experience one year later. Results of the survey validated the assignment's lasting value to the students across the cognitive, psychomotor, and affective learning domains. 相似文献
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In the design process of berm breakwaters, their front slope recession has an inevitable rule in large number of model tests, and this parameter being studied. This research draws its data from Moghim’s and Shekari’s experiment results. These experiments consist of two different 2D model tests in two wave flumes, in which the berm recession to different sea state and structural parameters have been studied. Irregular waves with a JONSWAP spectrum were used in both test series. A total of 412 test results were used to cover the impact of sea state conditions such as wave height, wave period, storm duration and water depth at the toe of the structure, and structural parameters such as berm elevation from still water level, berm width and stone diameter on berm recession parameters. In this paper, a new set of equations for berm recession is derived using the M5'' model tree as a machine learning approach. A comparison is made between the estimations by the new formula and the formulae recently given by other researchers to show the preference of new M5'' approach. 相似文献
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把地质大数据和人工智能技术引入矿产资源定量评价及成矿预测体系中,提高了海量地质数据的有效信息挖掘,弥补了传统方法的不足。本文基于白象山矿区基础地质资料和物化探成果资料,利用三维地质体建模技术和三维空间分析技术,量化三维控矿因素,建立了一种基于CART 算法的三维成矿预测模型。通过在白象山矿区的实验表明:该模型能较好的定位已知矿体,并且预测出在已知矿体北部、东部、东北部、西部、南部和东南部具有较高的成矿概率,可圈定找矿靶区。该模型将地质大数据应用于找矿勘探工作,具有纯数据驱动、预测精度高、预测结果可靠等优点。研究发现,该模型的预测效果与训练数据集的数量、矿控因素提取、决策树深度等有关。 相似文献