Fusion of Airborne LiDAR Point Clouds and Aerial Images for Heterogeneous Land-Use Urban Mapping
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-02-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/13/4/814 |
| _version_ | 1797395529282355200 |
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| author | Yasmine Megahed Ahmed Shaker Wai Yeung Yan |
| author_facet | Yasmine Megahed Ahmed Shaker Wai Yeung Yan |
| author_sort | Yasmine Megahed |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T00:36:50Z |
| format | Article |
| id | doaj.art-c6da364ee8764ffe92cb37fd83d0d203 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T00:36:50Z |
| publishDate | 2021-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-c6da364ee8764ffe92cb37fd83d0d2032023-12-11T18:08:23ZengMDPI AGRemote Sensing2072-42922021-02-0113481410.3390/rs13040814Fusion of Airborne LiDAR Point Clouds and Aerial Images for Heterogeneous Land-Use Urban MappingYasmine Megahed0Ahmed Shaker1Wai Yeung Yan2Department of Civil Engineering, Ryerson University, Toronto, ON M5B 2K3, CanadaDepartment of Civil Engineering, Ryerson University, Toronto, ON M5B 2K3, CanadaDepartment of Civil Engineering, Ryerson University, Toronto, ON M5B 2K3, CanadaThe 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.https://www.mdpi.com/2072-4292/13/4/814urban land-useLiDAR-aerial integrationLiDAR-aerial geo-registrationLiDAR classificationsupervised machine learningmaximum likelihood |
| spellingShingle | Yasmine Megahed Ahmed Shaker Wai Yeung Yan Fusion of Airborne LiDAR Point Clouds and Aerial Images for Heterogeneous Land-Use Urban Mapping Remote Sensing urban land-use LiDAR-aerial integration LiDAR-aerial geo-registration LiDAR classification supervised machine learning maximum likelihood |
| title | Fusion of Airborne LiDAR Point Clouds and Aerial Images for Heterogeneous Land-Use Urban Mapping |
| title_full | Fusion of Airborne LiDAR Point Clouds and Aerial Images for Heterogeneous Land-Use Urban Mapping |
| title_fullStr | Fusion of Airborne LiDAR Point Clouds and Aerial Images for Heterogeneous Land-Use Urban Mapping |
| title_full_unstemmed | Fusion of Airborne LiDAR Point Clouds and Aerial Images for Heterogeneous Land-Use Urban Mapping |
| title_short | Fusion of Airborne LiDAR Point Clouds and Aerial Images for Heterogeneous Land-Use Urban Mapping |
| title_sort | fusion of airborne lidar point clouds and aerial images for heterogeneous land use urban mapping |
| topic | urban land-use LiDAR-aerial integration LiDAR-aerial geo-registration LiDAR classification supervised machine learning maximum likelihood |
| url | https://www.mdpi.com/2072-4292/13/4/814 |
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