The constantly increasing amount and availability of urban data derived from varying sources leads to an assortment of challenges that include, among others, the consolidation, visualization, and maximal exploitation prospects of the aforementioned data. A preeminent problem affecting urban planning is the appropriate choice of location to host a particular activity (either commercial or common welfare service) or the correct use of an existing building or empty space. In this paper, we propose an approach to address these challenges availed with machine learning techniques. The proposed system combines, fuses, and merges various types of data from different sources, encodes them using a novel semantic model that can capture and utilize both low-level geometric information and higher level semantic information and subsequently feeds them to the random forests classifier, as well as other supervised machine learning models for comparisons. Our experimental evaluation on multiple real-world data sets comparing the performance of several classifiers (including Feedforward Neural Networks, Support Vector Machines, Bag of Decision Trees, k-Nearest Neighbors and Naïve Bayes), indicated the superiority of Random Forests in terms of the examined performance metrics (Accuracy, Specificity, Precision, Recall, F-measure and G-mean).
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机译：来自不同来源的城市数据的不断增加和可用性导致各种挑战，其中包括上述数据的合并，可视化和最大开发前景。 A preeminent problem affecting urban planning is the appropriate choice of location to host a particular activity (either commercial or common welfare service) or the correct use of an existing building or empty space.在本文中，我们提出了一种解决这些挑战的方法，可以使用机器学习技术。所提出的系统组合，保险丝和合并来自不同源的各种类型的数据，使用新颖的语义模型来编码，这些模型可以捕获和利用低级几何信息和更高级别的语义信息，然后将它们馈送到随机林类分类器，以及其他监督机器学习模型进行比较。我们对多个现实数据集的实验评估比较了多个分类器的性能（包括前馈神经网络，支持向量机，决策树，K-CORMONT邻居和天真贝叶斯的袋子），表明了随机森林的优越性检查了性能指标（准确性，特殊性，精度，召回，F测量和G-均值）。
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