Crop Classification On Cropharvest Kenya
Métriques
Average Accuracy
F1 Macro
Résultats
Résultats de performance de divers modèles sur ce benchmark
Nom du modèle | Average Accuracy | F1 Macro | Paper Title | Repository |
|---|---|---|---|---|
| Radar TS with TempCNN | 0.676 | 0.684 | In the Search for Optimal Multi-view Learning Models for Crop Classification with Global Remote Sensing Data | |
| PrestoR - no DW | - | - | Lightweight, Pre-trained Transformers for Remote Sensing Timeseries | |
| Feature-level fusion (sum) | 0.630 | - | A Comparative Assessment of Multi-view fusion learning for Crop Classification | |
| Input Fusion with TAE | 0.673 | 0.672 | In the Search for Optimal Multi-view Learning Models for Crop Classification with Global Remote Sensing Data | |
| Gated Fusion (Feature-level) | 0.665 | - | A Comparative Assessment of Multi-view fusion learning for Crop Classification |
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