Using Machine Learning to Extract Building Inventory Information Based on LiDAR Data
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Data
2.3. Methods
2.3.1. Image Segmentation and Analysis Dataset Preparation
2.3.2. Machine Learning Classification and Accuracy Assessment
2.3.3. Post-Processing
2.3.4. Building Heights and Footprint Area
3. Results
3.1. Variable Importance
3.2. Classification Results
3.3. Building Heights and Footprint Area
4. Discussion
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Type | Variables |
---|---|
Layers | Mean values of; intensity, slope, DSM, DSM–DTM difference, number of returns. Standard deviation of; intensity, slope, DSM, DSM–DTM difference, number of return. |
Geometric | Length/width, asymmetry, compactness, density, elliptic fit, rectangular fit, roundness. |
Textural | GLCM homogeneity of slope (GHom_slope), GLCM homogeneity of DSM (GHom_DSM), GLCM dissimilarity of slope (GDis_slope), GLCM dissimilarity of DSM (GDis_DSM), GLCM entropy of slope (GEnt_slope), GLCM entropy of DSM (GEnt_DSM), GLCM angular 2nd moment of slope (GAng2_slope), GLCM angular 2nd moment of DSM (GAng2_DSM). |
Data Split | Method | CCI | TP Rate | FP Rate | Precision | Recall | F-Score | ROC Area | PRC Area | |
---|---|---|---|---|---|---|---|---|---|---|
All variables | 70–30 | RF | 99.0 | 0.99 | 0.02 | 0.98 | 0.99 | 0.98 | 0.99 | 1.00 |
RT | 97.7 | 0.96 | 0.04 | 0.95 | 0.96 | 0.95 | 0.95 | 0.92 | ||
OF | 99.1 | 0.96 | 0.02 | 0.99 | 0.96 | 0.98 | 1.00 | 1.00 | ||
30–70 | RF | 98.8 | 0.97 | 0.03 | 0.98 | 0.97 | 0.97 | 1.00 | 1.00 | |
RT | 97.4 | 0.95 | 0.06 | 0.95 | 0.95 | 0.94 | 0.94 | 0.91 | ||
OF | 98.8 | 0.97 | 0.03 | 0.98 | 0.97 | 0.97 | 1.00 | 1.00 | ||
MDA | 70–30 | RF | 98.9 | 0.99 | 0.02 | 0.97 | 0.98 | 0.96 | 0.99 | 1.00 |
RT | 98.0 | 0.96 | 0.04 | 0.96 | 0.96 | 0.96 | 0.96 | 0.93 | ||
OF | 99.1 | 0.99 | 0.02 | 0.98 | 0.99 | 0.98 | 1.00 | 1.00 | ||
30–70 | RF | 98.6 | 0.97 | 0.04 | 0.98 | 0.97 | 0.97 | 1.00 | 1.00 | |
RT | 98.1 | 0.96 | 0.04 | 0.96 | 0.96 | 0.96 | 0.96 | 0.93 | ||
OF | 98.6 | 0.97 | 0.04 | 0.98 | 0.97 | 0.97 | 1.00 | 1.00 | ||
MDG | 70–30 | RF | 98.9 | 0.98 | 0.03 | 0.97 | 0.98 | 0.98 | 1.00 | 1.00 |
RT | 97.8 | 0.96 | 0.04 | 0.95 | 0.96 | 0.96 | 0.96 | 0.92 | ||
OF | 99.1 | 0.98 | 0.03 | 0.97 | 0.97 | 0.98 | 1.00 | 0.99 | ||
30–70 | RF | 98.6 | 0.97 | 0.04 | 0.97 | 0.97 | 0.97 | 1.00 | 1.00 | |
RT | 98.1 | 0.96 | 0.05 | 0.96 | 0.96 | 0.96 | 0.96 | 0.93 | ||
OF | 98.7 | 0.98 | 0.03 | 0.98 | 0.97 | 0.97 | 1.00 | 1.00 |
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Kaplan, G.; Comert, R.; Kaplan, O.; Matci, D.K.; Avdan, U. Using Machine Learning to Extract Building Inventory Information Based on LiDAR Data. ISPRS Int. J. Geo-Inf. 2022, 11, 517. https://doi.org/10.3390/ijgi11100517
Kaplan G, Comert R, Kaplan O, Matci DK, Avdan U. Using Machine Learning to Extract Building Inventory Information Based on LiDAR Data. ISPRS International Journal of Geo-Information. 2022; 11(10):517. https://doi.org/10.3390/ijgi11100517
Chicago/Turabian StyleKaplan, Gordana, Resul Comert, Onur Kaplan, Dilek Kucuk Matci, and Ugur Avdan. 2022. "Using Machine Learning to Extract Building Inventory Information Based on LiDAR Data" ISPRS International Journal of Geo-Information 11, no. 10: 517. https://doi.org/10.3390/ijgi11100517