Land Cover Classification on Berlin dataset benchmark leaderboard · SOTA2 Research

Land Cover Classification on Berlin dataset

90.2Accuracy (Forest)MGMNet

Evaluation Results

Method	Links
MGMNet2026.04		90.2	88.52	22.84	74.46	75.42	54.24	3.38	9.64	77.13	52.34	0.6243
GCCQTNet2026.04		87.46	77.74	54.34	71.62	72.8	67.39	20.42	57.94	73.34	63.71	0.6052
FusAtNet2026.04		86.24	91.38	19.76	20	48.72	38.89	18.47	29.61	70.91	44.13	0.5107
CHNet2026.04		84.84	81.14	56.53	78.24	79.32	74.53	17.67	63.8	76.32	67.01	0.6444
HCT2026.04		81.89	76.04	54.14	85.83	77.7	74.93	35.79	63.46	74.79	68.72	0.6303
MICF-Net2026.04		81.77	80.28	58.87	88.98	84.13	31.94	20.32	52.41	75.88	62.34	0.635
S^2ENet2026.04		81.09	73.05	62.61	82.82	86.41	54.61	2.56	75.96	71.08	64.88	0.5802
MACN2026.04		79.23	80.27	42.99	79.95	76.98	76.21	31.3	57.55	75.41	65.56	0.6315
ExViT2026.04		78.01	74.05	39.48	84.15	88.03	70	38.18	56.41	72.63	66.04	0.6048
AsyFFNet2026.04		77.4	71.3	38.98	79.3	87.69	81.65	36.52	75.96	70.82	68.59	0.5853
RSCNet2026.04		73.52	88.07	46.6	83.42	90.72	24.35	12.99	76.13	78.19	61.98	0.6544