Post-Hurricane Damage Severity Classification at the Individual Tree Level Using Terrestrial Laser Scanning and Deep Learning
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. TLS Data Acquisition and Processing
2.3. CNN Models and Accuracy Assessment
3. Results
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Validation Data | Architecture | Overall Accuracy (OA) | Kappa Statistic | Mean F1-Score |
---|---|---|---|---|
Binary color (Black and white) | DenseNet201 | 0.8725 | 0.84631 | 0.87107 |
EfficientNet_b7 | 0.8435 | 0.85814 | 0.88065 | |
Inception_v3 | 0.8774 | 0.85228 | 0.87754 | |
ResNet152v2 | 0.8725 | 0.84631 | 0.87027 | |
Simple CNN | 0.8823 | 0.85828 | 0.88307 | |
VGG16 | 0.8922 | 0.86995 | 0.89321 | |
Multicolored by height | DenseNet201 | 0.8725 | 0.84637 | 0.87253 |
EfficientNet_b7 | 0.8627 | 0.83403 | 0.84620 | |
Inception_v3 | 0.9020 | 0.88166 | 0.89777 | |
ResNet152v2 | 0.9012 | 0.88182 | 0.90512 | |
Simple CNN | 0.7353 | 0.67936 | 0.70929 | |
VGG16 | 0.9216 | 0.90556 | 0.92337 |
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Klauberg, C.; Vogel, J.; Dalagnol, R.; Ferreira, M.P.; Hamamura, C.; Broadbent, E.; Silva, C.A. Post-Hurricane Damage Severity Classification at the Individual Tree Level Using Terrestrial Laser Scanning and Deep Learning. Remote Sens. 2023, 15, 1165. https://doi.org/10.3390/rs15041165
Klauberg C, Vogel J, Dalagnol R, Ferreira MP, Hamamura C, Broadbent E, Silva CA. Post-Hurricane Damage Severity Classification at the Individual Tree Level Using Terrestrial Laser Scanning and Deep Learning. Remote Sensing. 2023; 15(4):1165. https://doi.org/10.3390/rs15041165
Chicago/Turabian StyleKlauberg, Carine, Jason Vogel, Ricardo Dalagnol, Matheus Pinheiro Ferreira, Caio Hamamura, Eben Broadbent, and Carlos Alberto Silva. 2023. "Post-Hurricane Damage Severity Classification at the Individual Tree Level Using Terrestrial Laser Scanning and Deep Learning" Remote Sensing 15, no. 4: 1165. https://doi.org/10.3390/rs15041165