Temporally generalizable land cover classification: A recurrent convolutional neural network unveils major coastal change through time

Keyphrases

• Coastal Change 100%
• Recurrent Convolutional Neural Network 100%
• Land Cover Classification 100%
• Land Cover 66%
• Low-lying Coastal Areas 66%
• Random Forest 33%
• Climate Change 33%
• Deep Learning Methods 33%
• Climate Change Impacts 33%
• World Population 33%
• Coastal Area 33%
• Support Vector Machine Model 33%
• Sea Level Rise 33%
• Agricultural Land 33%
• Coastal Plain 33%
• Major Change 33%
• Training Data 33%
• Deep Learning Model 33%
• Spatiotemporal Pattern 33%
• Low-lying 33%
• Validation Data 33%
• Temporal Context 33%
• Spectral-spatial Feature 33%
• Spectro-temporal 33%
• North Carolina 33%
• Spatial Context 33%
• Land Cover Classes 33%
• Decadal Cycle 33%
• Ghost Forest 33%
• Landsat 5 33%
• Wetland Forests 33%
• Disturbance Event 33%

Earth and Planetary Sciences

• Land Cover 100%
• Coastal Region 75%
• Climate Change 50%
• Coastal Plain 25%
• United States of America 25%
• Sea Level Rise 25%
• Farmland 25%
• Spatiotemporal Patterns 25%
• North Carolina 25%
• Support Vector Machine 25%
• Landsat 5 25%