Crop Classification on CropHarvest - Kenya

Metric: Average Accuracy (higher is better)

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#	Model↕	Average Accuracy▼	Extra Data	Paper	Date↕	Code
1	Radar TS with TempCNN	0.676	No	In the Search for Optimal Multi-view Learning Mo...	2024-03-25	Code
2	Input Fusion with TAE	0.673	No	In the Search for Optimal Multi-view Learning Mo...	2024-03-25	Code
3	Gated Fusion (Feature-level)	0.665	No	A Comparative Assessment of Multi-view fusion le...	2023-08-10	Code
4	Feature-level fusion (sum)	0.63	No	A Comparative Assessment of Multi-view fusion le...	2023-08-10	Code

#1Radar TS with TempCNNSOTA
0.676
Average Accuracy· 2024-03-25
In the Search for Optimal Multi-view Learning Models for Crop Classification with Global Remote Sensing Data Code
#2Input Fusion with TAE
0.673
Average Accuracy· 2024-03-25
In the Search for Optimal Multi-view Learning Models for Crop Classification with Global Remote Sensing Data Code
#3Gated Fusion (Feature-level)SOTA
0.665
Average Accuracy· 2023-08-10
A Comparative Assessment of Multi-view fusion learning for Crop Classification Code
#4Feature-level fusion (sum)
0.63
Average Accuracy· 2023-08-10
A Comparative Assessment of Multi-view fusion learning for Crop Classification Code