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Title: Fusion of airborne LiDAR point clouds and aerial images for heterogeneous land-use urban mapping
Authors: Megahed, Y
Shaker, A
Yan, WY 
Issue Date: 2-Feb-2021
Source: Remote sensing, 2 Feb. 2021, v. 13, no. 4, 814, p. 1-36
Abstract: 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.
Keywords: Urban land-use
LiDAR-aerial integration
LiDAR-aerial geo-registration
LiDAR classification
Supervised machine learning
Maximum likelihood
Support vector machines
Neural networks
Bootstrap aggregation
k-fold cross-validation
Publisher: Molecular Diversity Preservation International (MDPI)
Journal: Remote sensing 
EISSN: 2072-4292
DOI: 10.3390/rs13040814
Rights: Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (
The following publication Megahed, Y.; Shaker, A.; Yan, W.Y. Fusion of Airborne LiDAR Point Clouds and Aerial Images for Heterogeneous Land-Use Urban Mapping. Remote Sens. 2021, 13, 814 is available at
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