Semantic Segmentation on Potsdam (mIoU, mACC)
45.91mIoUDR-Seg
Evaluation results
| Method | Links | ||
|---|---|---|---|
| DR-SegBackbone=ViT-L, Training Dataset=DLRSD2026.04 | 45.91 | 60.14 | |
| DR-SegBackbone=ViT-B, Training Dataset=DLRSD2026.04 | 41.55 | 57.69 | |
| GSNetBackbone=ViT-L, Training Dataset=DLRSD2026.04 | 37.85 | 52.35 | |
| SANBackbone=ViT-L, Training Dataset=DLRSD2026.04 | 37.25 | 46.28 | |
| RSKT-SegBackbone=ViT-L, Training Dataset=DLRSD2026.04 | 37.14 | 53.75 | |
| OVRSBackbone=ViT-L, Training Dataset=DLRSD2026.04 | 36.44 | 50.17 | |
| Cat-SegBackbone=ViT-L, Training Dataset=DLRSD2026.04 | 35.75 | 49.03 | |
| RSKT-SegBackbone=ViT-B, Training Dataset=DLRSD2026.04 | 34.53 | 50.71 | |
| SANBackbone=ViT-B, Training Dataset=DLRSD2026.04 | 30.3 | 44.98 | |
| DR-SegBackbone=ViT-L, Training Dataset=iSAID2026.04 | 29.67 | 52.9 | |
| SEDBackbone=ConvNeXt-L, Training Dataset=DLRSD2026.04 | 29.35 | 37.95 | |
| RSKT-SegBackbone=ViT-L, Training Dataset=iSAID2026.04 | 28.57 | 51.69 | |
| GSNetBackbone=ViT-L, Training Dataset=iSAID2026.04 | 28.5 | 52 | |
| SCANBackbone=ViT-L, Training Dataset=iSAID2026.04 | 28.32 | 52.47 | |
| OVRSBackbone=ViT-B, Training Dataset=DLRSD2026.04 | 27.47 | 42.07 | |
| SCANBackbone=ViT-L, Training Dataset=DLRSD2026.04 | 27.45 | 39.22 | |
| Cat-SegBackbone=ViT-B, Training Dataset=DLRSD2026.04 | 26.79 | 44.72 | |
| GSNetBackbone=ViT-B, Training Dataset=DLRSD2026.04 | 26.46 | 43.2 | |
| OVRSBackbone=ViT-L, Training Dataset=iSAID2026.04 | 26.39 | 50.15 | |
| SANBackbone=ViT-L, Training Dataset=iSAID2026.04 | 24.72 | 56.54 | |
| Cat-SegBackbone=ViT-L, Training Dataset=iSAID2026.04 | 23.9 | 49.49 | |
| RSKT-SegBackbone=ViT-B, Training Dataset=iSAID2026.04 | 20.28 | 46.71 | |
| SCANBackbone=ViT-B, Training Dataset=DLRSD2026.04 | 20.22 | 34.7 | |
| SEDBackbone=ConvNeXt-B, Training Dataset=DLRSD2026.04 | 19.47 | 33.4 | |
| DR-SegBackbone=ViT-B, Training Dataset=iSAID2026.04 | 19.4 | 41.24 | |
| SCANBackbone=ViT-B, Training Dataset=iSAID2026.04 | 18.25 | 33.17 | |
| OVRSBackbone=ViT-B, Training Dataset=iSAID2026.04 | 15.57 | 38.94 | |
| Cat-SegBackbone=ViT-B, Training Dataset=iSAID2026.04 | 15.23 | 37.17 | |
| GSNetBackbone=ViT-B, Training Dataset=iSAID2026.04 | 15.12 | 36.16 | |
| SANBackbone=ViT-B, Training Dataset=iSAID2026.04 | 14.82 | 34.84 | |
| SEDBackbone=ConvNeXt-L, Training Dataset=iSAID2026.04 | 11.85 | 23.87 | |
| SEDBackbone=ConvNeXt-B, Training Dataset=iSAID2026.04 | 5.78 | 17.52 |