School of Civil Engineering and Architecture, Xiamen University of Technology, Xiamen 361024, China.
The Arts and Design College Xiamen, Fuzhou University, Xiamen 361000, China.
Sensors (Basel). 2023 Jun 5;23(11):5353. doi: 10.3390/s23115353.
There are high concentrations of urban spaces and increasingly complex land use types. Providing an efficient and scientific identification of building types has become a major challenge in urban architectural planning. This study used an optimized gradient-boosted decision tree algorithm to enhance a decision tree model for building classification. Through supervised classification learning, machine learning training was conducted using a business-type weighted database. We innovatively established a form database to store input items. During parameter optimization, parameters such as the number of nodes, maximum depth, and learning rate were gradually adjusted based on the performance of the verification set to achieve optimal performance on the verification set under the same conditions. Simultaneously, a k-fold cross-validation method was used to avoid overfitting. The model clusters trained in the machine learning training corresponded to various city sizes. By setting the parameters to determine the size of the area of land for a target city, the corresponding classification model could be invoked. The experimental results show that this algorithm has high accuracy in building recognition. Especially in R, S, and U-class buildings, the overall accuracy rate of recognition reaches over 94%.
存在高度集中的城市空间和日益复杂的土地利用类型。为建筑物类型提供高效、科学的识别已成为城市建筑规划的主要挑战。本研究使用优化的梯度提升决策树算法增强了建筑物分类的决策树模型。通过有监督的分类学习，使用商业加权数据库对机器学习进行训练。我们创新性地建立了一个表单数据库来存储输入项。在参数优化过程中，根据验证集的性能逐步调整节点数量、最大深度和学习率等参数，以在相同条件下在验证集上达到最佳性能。同时，使用 k 折交叉验证方法避免过拟合。在机器学习训练中聚类的模型对应于不同的城市规模。通过设置参数来确定目标城市的土地面积大小，可以调用相应的分类模型。实验结果表明，该算法在建筑物识别方面具有很高的准确性。特别是在 R、S 和 U 类建筑物中，识别的整体准确率达到 94%以上。
