Automatic generation of land use maps using aerial orthoimages and building floor data with a Conv-Depth Block (CDB) ResU-Net architecture

Earth and Planetary Sciences

• Land Use 100%
• Map 83%
• Depth 66%
• Datum 50%
• Building 50%
• Input 50%
• Block 50%
• Floor 50%
• Metropolitan Area 33%
• Accuracy 33%
• Network 33%
• Architecture 33%
• Estimating 16%
• State of the Art 16%
• Versatility 16%
• Investigation 16%
• Information 16%
• Risk 16%
• Korea 16%
• Output 16%
• Sample 16%
• Context 16%
• Creativity 16%
• Land Classification 16%

Computer Science

• Deep Learning 33%
• Networks 33%
• Accuracy 33%
• Automatic Generation 16%
• Learning Approach 16%
• Topographic Map 16%
• Convolution Layer 16%
• Generation 16%
• Contexts 16%
• Classes 16%