Evaluating the ability of convolutional neural networks for transfer learning in Pinus radiata cover predictions

Keyphrases

• Pinus Radiata 100%
• Convolutional Neural Network 100%
• Transfer Learning 100%
• Spatial Variability 66%
• High Spatial 33%
• Chile 16%
• Native Forest 16%
• Vehicle Data 16%
• Transferability 16%
• Transference 16%
• Best Model 16%
• Plant Invasion 16%
• Regression Approach 16%
• Drone 16%
• Robust Approach 16%
• High-resolution Imagery 16%
• Coefficient of Determination 16%
• P. Radiata 16%
• Site-specific Model 16%
• High-resolution Remote Sensing Data 16%
• Testing Data 16%
• Domain Generalization 16%
• Convolutional Neural Network Model 16%
• Data Domain 16%
• Multi-site Model 16%
• Testing Scheme 16%
• Unpiloted Aerial Vehicles 16%
• Convolutional Neural Network Architecture 16%
• Test Validity 16%
• Model Training 16%

INIS

• data 100%
• learning 100%
• neural networks 100%
• prediction 100%
• architecture 28%
• testing 28%
• resolution 28%
• chile 14%
• comparative evaluations 14%
• ecosystems 14%
• performance 14%
• information 14%
• evaluation 14%
• forests 14%
• trains 14%
• assessments 14%
• validation 14%
• remote sensing 14%
• investigations 14%
• vehicles 14%
• extrapolation 14%
• drone 14%

Computer Science

• Transfer Learning 100%
• Convolutional Neural Network 100%
• Neural Network Model 16%
• Neural Network Architecture 16%
• Data Domain 16%
• high resolution remote sensing 16%
• Wide Variability 16%
• Robust Approach 16%
• Drone 16%