Locating Charcoal Production Sites in Sweden Using LiDAR, Hydrological Algorithms, and Deep Learning
Abstract
:1. Introduction
2. Automation in Archaeological Remote Sensing
A Solution to Training Data Issues: Hydrological Depression Algorithms
3. Materials and Methods
3.1. Depression Algorithm
3.2. Deep Learning with RetinaNet
3.3. Implementing RetinaNet CNN in ArcGIS Pro
4. Results
4.1. Depression Analysis Results
4.2. RetinaNet Results
5. Discussion
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Transfer Learning Architecture | Learning Rate | Accuracy | Training Loss | Validation Loss |
---|---|---|---|---|
ResNet34 | 8.3 × 10−5 | 59% | 1.7295 | 1.9112 |
ResNet50 | 6.3 × 10−6 | 63% | 1.8222 | 1.2973 |
ResNet101 | 0.0001 | 55% | 0.8254 | 1.0824 |
ResNet152 | 4.8 × 10−5 | 61% | 0.6316 | 0.8489 |
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Davis, D.S.; Lundin, J. Locating Charcoal Production Sites in Sweden Using LiDAR, Hydrological Algorithms, and Deep Learning. Remote Sens. 2021, 13, 3680. https://doi.org/10.3390/rs13183680
Davis DS, Lundin J. Locating Charcoal Production Sites in Sweden Using LiDAR, Hydrological Algorithms, and Deep Learning. Remote Sensing. 2021; 13(18):3680. https://doi.org/10.3390/rs13183680
Chicago/Turabian StyleDavis, Dylan S., and Julius Lundin. 2021. "Locating Charcoal Production Sites in Sweden Using LiDAR, Hydrological Algorithms, and Deep Learning" Remote Sensing 13, no. 18: 3680. https://doi.org/10.3390/rs13183680