Attention-Based Semantic Segmentation Networks for Forest Applications

Keyphrases

• Deforestation 100%
• Satellite Imagery 100%
• Attention Mechanism 100%
• Attention-based 100%
• Semantic Segmentation Network 100%
• Forestry Applications 100%
• HRNet 100%
• Early Detection 50%
• Benchmarking Analysis 50%
• Performance Metrics 50%
• High-resolution 50%
• Remote Sensing Technology 50%
• Remote Sensing 50%
• Forest Area 50%
• Architecture-centric 50%
• Hyperparameters 50%
• Deep Learning Methods 50%
• Google Earth 50%
• High-resolution Feature 50%
• Attention Model 50%
• Feature Map 50%
• Deep Network 50%
• U-Net 50%
• Rainforest 50%
• Mean Intersection over Union 50%
• Efficient Monitoring 50%
• Band Combination 50%
• Semantic Segmentation 50%
• Fully Convolutional Neural Network 50%
• National Forests 50%
• High-resolution Representation 50%
• Two-class Problems 50%
• Land Urbanization 50%
• Segmentation Architecture 50%
• Sensing Tasks 50%
• FC-DenseNet 50%
• Agricultural Land Expansion 50%
• Effective Monitoring 50%
• Slice Image 50%
• Forest Identification 50%
• SegNet 50%

Computer Science

• Image Segmentation 100%
• Attention (Machine Learning) 100%
• Convolutional Neural Network 50%
• Early Detection 50%
• Performance Metric 50%
• Benchmarking 50%
• Deep Learning Technique 50%
• DenseNet 50%
• U-Net 50%
• Baseline Architecture 50%

Earth and Planetary Sciences

• Deforestation 100%
• Satellite Imagery 100%
• Farmland 50%
• Remote Sensing 50%
• Remote Sensing Technology 50%