The World Health Organization has reported that the number of worldwide urban residents is expected to reach 70% of the total world population by 2050. In the face of challenges brought about by the demographic transition, there is an urgent need to improve the accuracy of urban land-use mappings to more efficiently inform about urban planning processes. Decision-makers rely on accurate urban mappings to properly assess current plans and to develop new ones. This study investigates the effects of including conventional spectral signatures acquired by different sensors on the classification of airborne LiDAR (Light Detection and Ranging) point clouds using multiple feature spaces. The proposed method applied three machine learning algorithms—ML (Maximum Likelihood), SVM (Support Vector Machines), and MLP (Multilayer Perceptron Neural Network)—to classify LiDAR point clouds of a residential urban area after being geo-registered to aerial photos. The overall classification accuracy passed 97%, with height as the only geometric feature in the classifying space. Misclassifications occurred among different classes due to independent acquisition of aerial and LiDAR data as well as shadow and orthorectification problems from aerial images. Nevertheless, the outcomes are promising as they surpassed those achieved with large geometric feature spaces and are encouraging since the approach is computationally reasonable and integrates radiometric properties from affordable sensors.

中文翻译：

---

机载LiDAR点云和航空图像的融合，用于异类土地利用城市制图

世界卫生组织报告说，到2050年，全世界城市居民的数量预计将达到世界总人口的70％。面对人口转变带来的挑战，迫切需要提高城市人口的准确性。土地利用图，以更有效地告知城市规划过程。决策者依靠准确的城市地图来正确评估当前计划并制定新计划。这项研究调查了使用不同特征空间将不同传感器获取的常规光谱特征包括在内对机载LiDAR（光检测和测距）点云分类的影响。所提出的方法应用了三种机器学习算法-ML（最大似然），SVM（支持向量机），和MLP（多层感知器神经网络）—在对航空照片进行地理注册后，对居民区的LiDAR点云进行分类。总体分类精度超过了97％，高度是分类空间中唯一的几何特征。由于对航空和LiDAR数据的独立采集以及航拍图像的阴影和正射校正问题，不同类别之间发生了错误分类。但是，结果超过了通过大型几何特征空间获得的结果，结果令人鼓舞，并且令人鼓舞，因为该方法在计算上是合理的，并且集成了可负担得起的传感器的辐射特性。高度是分类空间中唯一的几何特征。由于对航空和LiDAR数据的独立采集以及航拍图像的阴影和正射校正问题，不同类别之间发生了错误分类。但是，结果超过了通过大型几何特征空间获得的结果，结果令人鼓舞，并且令人鼓舞，因为该方法在计算上是合理的，并且集成了可负担得起的传感器的辐射特性。高度是分类空间中唯一的几何特征。由于对航空和LiDAR数据的独立采集以及航拍图像的阴影和正射校正问题，不同类别之间发生了错误分类。但是，结果超过了通过大型几何特征空间获得的结果，结果令人鼓舞，并且令人鼓舞，因为该方法在计算上是合理的，并且集成了可负担得起的传感器的辐射特性。