Sentinel-1 Spatiotemporal Simulation Using Convolutional LSTM for Flood Mapping

Keyphrases

• Synthetic Images 100%
• Flood Mapping 100%
• Sentinel-1 100%
• Convolutional Long Short-term Memory (ConvLSTM) 100%
• Spatio-temporal Modeling 100%
• Change Detection 75%
• Synthetic Aperture Radar Imagery 75%
• Mozambique 75%
• Australia 50%
• Brazil 50%
• Sentinel 50%
• Kappa Coefficient 25%
• Optical Data 25%
• Satellite Data 25%
• Agricultural Land 25%
• Divergence 25%
• Classification Accuracy 25%
• Detection Method 25%
• Land Cover Change 25%
• Spatially Correlated 25%
• Flood Events 25%
• Long Short-term Memory 25%
• Long Short-term Memory Model 25%
• Deep Learning Methods 25%
• Synthetic Aperture Radar Image 25%
• Simulation Framework 25%
• Topographic Effects 25%
• Series Analysis 25%
• Extreme Weather Events 25%
• Flooded Area 25%
• Forest Area 25%
• Urban Landscape 25%
• Crop Growth 25%
• Crop Harvest 25%
• Deep Learning Framework 25%
• Cohen's kappa 25%
• Planet Labs 25%
• Spatial Signature 25%
• Long Short-term Memory Architecture 25%
• Convolution Operation 25%
• Low-κ 25%
• Land Clearance 25%
• Correlated Information 25%

Earth and Planetary Sciences

• Synthetic Aperture Radar 100%
• Change Detection 75%
• Mozambique 75%
• Radar Imagery 50%
• Australia 50%
• Brazil 50%
• Radar Data 25%
• Satellite Data 25%
• Time Series 25%
• Farmland 25%
• Land Cover Change 25%
• Topographic Effect 25%
• Time Series Analysis 25%
• Urban Landscape 25%
• Crop Growth 25%
• Optical Property 25%