Research Article
Fire-Net: A Deep Learning Framework for Active Forest Fire Detection
Table 2
Accuracy assessment of active fire detection (Comparison of Fire-Net and MSR-U-Net).
| Method | OA (%) | Precision (%) | Recall (%) | FPR (%) | MD (%) | F1-score (%) | KC |
| (a) Australian’s Forest | MSR-U-net | 94.73 | 16.55 | 100 | 4.91 | 0.00 | 28.40 | 0.272 | Fire-net | 99.95 | 97.94 | 97.20 | 0.02 | 2.79 | 97.57 | 0.975 | (b) Central Africa’s Forest in 2018-12-19 | MSR-U-net | 99.99 | 72.91 | 70.70 | 0.0001 | 29.29 | 71.79 | 0.429 | Fire-net | 99.99 | 84.06 | 77.27 | 0.00007 | 22.72 | 80.52 | 0.429 | (c) Brazil‘s forest | MSR-U-net | 99.99 | 86.14 | 87.15 | 0.001 | 12.85 | 86.64 | 0.429 | Fire-net | 99.99 | 95.98 | 98.04 | 0.0004 | 1.95 | 97.00 | 0.429 | (d) Chernobyl | MSR-U-net | 99.99 | 86.14 | 87.15 | 0.004 | 15.45 | 81.96 | 0.429 | Fire-net | 99.99 | 95.98 | 98.04 | 0.0006 | 4.58 | 97.24 | 0.429 |
|
|