The Use of Machine Learning Algorithms in Urban Tree Species Classification
Trees are the key components of urban vegetation in cities. The timely and accurate identification of existing urban tree species with their location is the most important task for improving air, water, and land quality; reducing carbon accumulation; mitigating urban heat island effects; and protect...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-03-01
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| Series: | ISPRS International Journal of Geo-Information |
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| Online Access: | https://www.mdpi.com/2220-9964/11/4/226 |
| _version_ | 1797434585697484800 |
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| author | Zehra Cetin Naci Yastikli |
| author_facet | Zehra Cetin Naci Yastikli |
| author_sort | Zehra Cetin |
| collection | DOAJ |
| description | Trees are the key components of urban vegetation in cities. The timely and accurate identification of existing urban tree species with their location is the most important task for improving air, water, and land quality; reducing carbon accumulation; mitigating urban heat island effects; and protecting soil and water balance. Light detection and ranging (LiDAR) is frequently used for extracting high-resolution structural information regarding tree objects. LiDAR systems are a cost-effective alternative to the traditional ways of identifying tree species, such as field surveys and aerial photograph interpretation. The aim of this work was to assess the usage of machine learning algorithms for classifying the deciduous (broadleaf) and coniferous tree species from 3D raw LiDAR data on the Davutpasa Campus of Yildiz Technical University, Istanbul, Turkey. First, ground, building, and low, medium, and high vegetation classes were acquired from raw LiDAR data using a hierarchical-rule-based classification method. Next, individual tree crowns were segmented using a mean shift clustering algorithm from high vegetation points. A total of 25 spatial- and intensity-based features were utilized for support vector machine (SVM), random forest (RF), and multi-layer perceptron (MLP) classifiers to discriminate deciduous and coniferous tree species in the urban area. The machine learning-based classification’s overall accuracies were 80%, 83.75%, and 73.75% for the SVM, RF, and MLP classifiers, respectively, in split 70/30 (training/testing). The SVM and RF algorithms generally gave better classification results than the MLP algorithm for identifying the urban tree species. |
| first_indexed | 2024-03-09T10:35:30Z |
| format | Article |
| id | doaj.art-c841602274db4a0e871a10c7658d9cf4 |
| institution | Directory Open Access Journal |
| issn | 2220-9964 |
| language | English |
| last_indexed | 2024-03-09T10:35:30Z |
| publishDate | 2022-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | ISPRS International Journal of Geo-Information |
| spelling | doaj.art-c841602274db4a0e871a10c7658d9cf42023-12-01T21:01:15ZengMDPI AGISPRS International Journal of Geo-Information2220-99642022-03-0111422610.3390/ijgi11040226The Use of Machine Learning Algorithms in Urban Tree Species ClassificationZehra Cetin0Naci Yastikli1Department of Geomatic Engineering, Faculty of Civil Engineering, Yildiz Technical University, Davutpasa Campus, Esenler, Istanbul 34220, TurkeyDepartment of Geomatic Engineering, Faculty of Civil Engineering, Yildiz Technical University, Davutpasa Campus, Esenler, Istanbul 34220, TurkeyTrees are the key components of urban vegetation in cities. The timely and accurate identification of existing urban tree species with their location is the most important task for improving air, water, and land quality; reducing carbon accumulation; mitigating urban heat island effects; and protecting soil and water balance. Light detection and ranging (LiDAR) is frequently used for extracting high-resolution structural information regarding tree objects. LiDAR systems are a cost-effective alternative to the traditional ways of identifying tree species, such as field surveys and aerial photograph interpretation. The aim of this work was to assess the usage of machine learning algorithms for classifying the deciduous (broadleaf) and coniferous tree species from 3D raw LiDAR data on the Davutpasa Campus of Yildiz Technical University, Istanbul, Turkey. First, ground, building, and low, medium, and high vegetation classes were acquired from raw LiDAR data using a hierarchical-rule-based classification method. Next, individual tree crowns were segmented using a mean shift clustering algorithm from high vegetation points. A total of 25 spatial- and intensity-based features were utilized for support vector machine (SVM), random forest (RF), and multi-layer perceptron (MLP) classifiers to discriminate deciduous and coniferous tree species in the urban area. The machine learning-based classification’s overall accuracies were 80%, 83.75%, and 73.75% for the SVM, RF, and MLP classifiers, respectively, in split 70/30 (training/testing). The SVM and RF algorithms generally gave better classification results than the MLP algorithm for identifying the urban tree species.https://www.mdpi.com/2220-9964/11/4/226machine learningclassificationLiDAR3D point cloudurban trees |
| spellingShingle | Zehra Cetin Naci Yastikli The Use of Machine Learning Algorithms in Urban Tree Species Classification ISPRS International Journal of Geo-Information machine learning classification LiDAR 3D point cloud urban trees |
| title | The Use of Machine Learning Algorithms in Urban Tree Species Classification |
| title_full | The Use of Machine Learning Algorithms in Urban Tree Species Classification |
| title_fullStr | The Use of Machine Learning Algorithms in Urban Tree Species Classification |
| title_full_unstemmed | The Use of Machine Learning Algorithms in Urban Tree Species Classification |
| title_short | The Use of Machine Learning Algorithms in Urban Tree Species Classification |
| title_sort | use of machine learning algorithms in urban tree species classification |
| topic | machine learning classification LiDAR 3D point cloud urban trees |
| url | https://www.mdpi.com/2220-9964/11/4/226 |
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