📊 BẢNG SO SÁNH KẾT QUẢ CÁC MÔ HÌNH
1. Nhóm Phân loại Lớp phủ (Land Classification)
| Model |
Accuracy |
Precision |
Recall |
F1-Score |
Parameters |
| XGBoost |
0.287611 |
0.353394 |
0.287611 |
0.234607 |
estimators:200, depth:6 |
| swin-unet |
0.724771 |
0.536633 |
0.59274 |
0.562804 |
N/A |
| swin-unet |
0.600917 |
0.569649 |
0.590866 |
0.564547 |
N/A |
| cnn |
0.507812 |
0.490742 |
0.474017 |
0.452274 |
N/A |
| swin-unet |
0.732283 |
0.722509 |
0.677869 |
0.660619 |
estimators:500, depth:256 |
| swin-unet |
0.203791 |
0.130717 |
0.167561 |
0.126147 |
N/A |
| LightGBM_Balanced |
0.185841 |
0.284595 |
0.185841 |
0.147651 |
estimators:300, depth:-1 |
| svm |
0.582677 |
0.441728 |
0.464997 |
0.433273 |
N/A |
| random_forest |
0.614173 |
0.503912 |
0.522185 |
0.509931 |
N/A |
| swin-unet |
0.724771 |
0.536905 |
0.59274 |
0.562949 |
N/A |
| swin-unet |
0.724771 |
0.537078 |
0.59274 |
0.562875 |
N/A |
| xgboost |
0.274336 |
0.297502 |
0.203779 |
0.146772 |
N/A |
| swin-unet |
0.720183 |
0.533799 |
0.589064 |
0.559531 |
N/A |
| xgboost |
0.637795 |
0.5391 |
0.546491 |
0.538521 |
N/A |
| swin-unet |
0.706422 |
0.523141 |
0.574641 |
0.547069 |
N/A |
| random_forest |
0.376106 |
|
|
|
N/A |
| swin-unet |
0.234375 |
0.0334821 |
0.142857 |
0.0542495 |
N/A |
| xgboost |
0.578125 |
0.568048 |
0.534613 |
0.541406 |
N/A |
| mobilenet-lraspp |
0.566929 |
0.543959 |
0.52518 |
0.477603 |
N/A |
| cnn |
0.574803 |
0.476253 |
0.454007 |
0.400213 |
N/A |
| xgboost |
0.756881 |
0.712478 |
0.700939 |
0.704615 |
N/A |
| mobilenet-lraspp |
0.692661 |
0.65895 |
0.691875 |
0.650169 |
N/A |
| RandomForest_RealData |
0.274336 |
0.330072 |
0.274336 |
0.215733 |
estimators:100, depth:15 |
| decision_tree |
0.590551 |
0.604639 |
0.613393 |
0.607498 |
N/A |
| lightgbm |
0.598425 |
0.479595 |
0.507168 |
0.488216 |
N/A |
2. Nhóm Xóa mây (Cloud Removal)
| Model |
Epochs |
Train Loss |
Val Loss |
| SwinUNet_Cloud_Removal |
100 |
0.008 |
0.009 |
| CNN_Cloud_Removal |
50 |
0.015 |
0.012 |
| SwinUNet_Cloud_Removal_RealData_GPU |
1 |
0.157653 |
0.157653 |
3. Nhóm Dự báo Thực vật (NDVI Forecasting)
| Model |
RMSE |
MAE |
Epochs |
| LSTM Time Series (Real Data & GPU) |
0.299411 |
0.0896472 |
50 |
| Hybrid Physics-ML (DSSAT/WOFOST) |
0.018 |
0.012 |
N/A |
| Multi-Model Ensemble (Real Data & CPU) |
0.119913 |
0.0936021 |
N/A |
| LSTM/GRU Time Series |
0.03 |
0.025 |
200 |
| Statistical (SARIMA) |
0.05 |
0.04 |
N/A |
| ConvLSTM Spatial-Temporal |
0.02 |
0.015 |
100 |
| ConvLSTM Spatial-Temporal (Real Data & GPU) |
0.312591 |
0.0977133 |
20 |
| Hybrid Physics-ML (Real Data & GPU) |
0.0010379 |
0.000608871 |
N/A |
| Multi-Model Ensemble |
0.015 |
0.01 |
N/A |