The Use of Machine Learning Algorithms in Urban Tree Species Classification
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. LiDAR Data and Field Data
2.3. General Workflow
2.4. High Vegetation Classification
2.5. The Segmentation of Individual Tree Crowns
2.6. Feature Extraction
2.7. The Classification of Urban Tree Species
2.7.1. Support Vector Machine
2.7.2. Random Forest
2.7.3. Multi-Layer Perceptron
2.8. Performance Analysis
3. Results and Discussion
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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LiDAR Data | |
---|---|
Spatial-Based Features | Intensity-Based Features |
Number of points | – |
Maximum Z | Maximum intensity |
Minimum Z | Minimum intensity |
Standard deviation of Z | Standard deviation of intensity |
Mean Z | Mean intensity |
Skewness of Z | Skewness of intensity |
Kurtosis of Z | Kurtosis of intensity |
Z range | Intensity range |
5th percentile of Z | 5th percentile of intensity |
25th percentile of Z | 25th percentile of intensity |
50th percentile of Z | 50th percentile of intensity |
75th percentile of Z | 75th percentile of intensity |
90th percentile of Z | 90th percentile of intensity |
Classifier | Overall Accuracy |
---|---|
SVM | 80.00% |
RF | 83.75% |
MLP | 73.75% |
Classifier | 10-Fold Cross-Validation Average Accuracy |
---|---|
SVM | 81.10% |
RF | 81.54% |
MLP | 63.72% |
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Cetin, Z.; Yastikli, N. The Use of Machine Learning Algorithms in Urban Tree Species Classification. ISPRS Int. J. Geo-Inf. 2022, 11, 226. https://doi.org/10.3390/ijgi11040226
Cetin Z, Yastikli N. The Use of Machine Learning Algorithms in Urban Tree Species Classification. ISPRS International Journal of Geo-Information. 2022; 11(4):226. https://doi.org/10.3390/ijgi11040226
Chicago/Turabian StyleCetin, Zehra, and Naci Yastikli. 2022. "The Use of Machine Learning Algorithms in Urban Tree Species Classification" ISPRS International Journal of Geo-Information 11, no. 4: 226. https://doi.org/10.3390/ijgi11040226