ABSTRACT

Research has developed numerous algorithms to simplify building data. Each algorithm has strengths and weaknesses in addressing shape characteristics, but no single algorithm can appropriately simplify all buildings. This study proposes a hybrid approach that identifies the best simplified representation of a building among four existing algorithms. The proposed approach applies the four algorithms to generate simplification candidates. With a backpropagation neural network, an evaluator is built through supervised learning based on measurements describing the changes in position, size, orientation, and shape between the original building and the candidates of its simplified representations. The evaluator determines the most appropriate candidate. Experiments on buildings from residential and commercial areas in Shenzhen city show that the hybrid approach can combine the advantages of different algorithms. The percentages of unreasonable simplified buildings in the results obtained using the hybrid algorithm are 3.8% in the residential area and 0 in the commercial area, respectively, which are significantly lower than those in the results of standalone applications of the four algorithms. Furthermore, comparison with the simplification algorithm in the popular software, ArcGIS, confirms that our approach shows better results in terms of corner squaring and maintaining the regional characteristics of buildings.

摘要

研究开发了许多算法来简化建筑数据。每种算法在处理形状特征方面都有优点和缺点，但没有一种算法可以适当地简化所有建筑物。本研究提出了一种混合方法，可在四种现有算法中确定建筑物的最佳简化表示。所提出的方法应用四种算法来生成简化候选。使用反向传播神经网络，评估器是通过监督学习构建的，该评估器基于描述原始建筑物与其简化表示的候选者之间的位置、大小、方向和形状的变化的测量值。评估人员确定最合适的候选人。对深圳市住宅区和商业区建筑物的实验表明，混合方法可以结合不同算法的优点。混合算法得到的结果中不合理的简化建筑比例在住宅区和商业区分别为3.8%和0，明显低于四种算法单独应用的结果。此外，与流行软件ArcGIS中的简化算法进行比较，证实我们的方法在角平方和保持建筑物的区域特征方面显示出更好的结果。住宅区为 8%，商业区为 0，明显低于四种算法独立应用的结果。此外，与流行软件ArcGIS中的简化算法进行比较，证实我们的方法在角平方和保持建筑物的区域特征方面显示出更好的结果。住宅区为 8%，商业区为 0，明显低于四种算法独立应用的结果。此外，与流行软件ArcGIS中的简化算法进行比较，证实我们的方法在角平方和保持建筑物的区域特征方面显示出更好的结果。