Multimodal Remote Sensing Classification on LCZ HK 50 samples per class (train) benchmark leaderboard · SOTA2 Research

Multimodal Remote Sensing Classification on LCZ HK 50 samples per class (train)

83.3Class 1 AccuracyMSFMamba

Evaluation Results

Method	Links
MSFMamba2025.09		83.3	90.4	87.6	88.49	73.64	96.38	86.84	93.42	88.57	96.55	92.31	94.89	61.02	86.9	87.38	97.57
BDGF2025.09		80.77	95.35	95.43	94.42	97.05	99.02	90.16	99.87	97.14	99.66	98.58	97.48	91.94	96.93	93.52	98.53
SS-MAE2025.09		78.14	93.16	92.59	91.79	89.15	92.03	93.94	94.74	98.57	100	97.63	95.59	90.82	90.02	84.17	99.1
CALC2025.09		77.62	91.98	90.09	90.38	66.67	97.1	93.74	94.74	100	93.1	96.45	98.08	87.76	91.11	77.22	97.61
NCGLF2025.09		76.4	92.51	92.36	91.16	90.67	93.83	87.31	99.68	99.33	95.84	96.67	97.17	87.02	94.63	85.01	97.07
HLMamba2025.09		74.87	88.26	87.41	85.95	96.12	93.48	76.08	89.47	98.57	83.06	94.08	93.04	80.41	87.99	68.66	98.55
Fusion-HCT2025.09		72.46	90.87	89.47	89.09	80.62	97.46	81.7	98.68	92.86	94.25	89.94	94.76	84.9	92.82	85.78	96.91
UACL2025.09		68.67	90.59	88.73	85.55	34.11	97.1	78.97	98.68	87.14	95.4	100	96.87	84.9	80.97	89.73	99.61
AsyFFNet2025.09		64.37	88.38	88.03	86.09	95.35	89.13	80.9	97.37	100	95.4	93.49	94.7	82.45	71.76	81.28	98.12
MACN2025.09		53.53	89.4	88.81	87.29	86.82	97.46	91.97	97.37	98.57	94.25	98.22	94.76	84.9	82.37	74.65	99.69