计算机科学
点云
人工智能
分类器(UML)
Boosting(机器学习)
特征提取
梯度升压
比例(比率)
机器学习
模式识别(心理学)
数据挖掘
随机森林
地理
地图学
作者
Eray Sevgen,Saygın Abdikan
出处
期刊:Remote Sensing
[MDPI AG]
日期:2023-07-30
卷期号:15 (15): 3787-3787
被引量:1
摘要
Automatic point cloud classification (PCC) is a challenging task in large-scale urban point clouds due to the heterogeneous density of points, the high number of points and the incomplete set of objects. Although recent PCC studies rely on automatic feature extraction through deep learning (DL), there is still a gap for traditional machine learning (ML) models with hand-crafted features, particularly after emerging gradient boosting machine (GBM) methods. In this study, we are using the traditional ML framework for the problem of PCC in large-scale datasets following the steps of neighborhood definition, multi-scale feature extraction, and classification. Different from others, our framework takes advantage of the fast feature calculation with multi-scale radius neighborhood and a recent state-of-the-art GBM classifier, LightGBM. We tested our framework using three mobile urban datasets, Paris–Rau–Madame, Paris–Rue–Cassette and Toronto3D. According to the results, our framework outperforms traditional machine learning models and competes with DL-based methods.
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