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The document presents MSU-Net, a multi-scale self-attention semantic segmentation method designed for accurately extracting oil-tea camellia planting areas in the hilly regions of southern China. The proposed model incorporates a self-attention mechanism and an Atrous Spatial Pyramid Pooling (ASPP) module, achieving high segmentation accuracy with an Intersection over Union (IOU) of 0.93 and Overall Accuracy (OA) of 94.83%. This approach addresses challenges posed by complex terrain and variable weather con

Author

Farel natan

Language

EN