| building-change-detection-for-remote-sensing | IMP-ViTAEv2-S-BIT | #14 | F1: 91.26 |
| building-change-detection-for-remote-sensing | RSP-ViTAEv2-S-BIT | #19 | F1: 90.93IoU: 84.95 |
| building-change-detection-for-remote-sensing | RSP-ResNet-50 | #23 | F1: 90.10 |
| building-change-detection-for-remote-sensing | RSP-Swin-T | #24 | F1: 90.10 |
| change-detection-for-remote-sensing-images-on | IMP-ViTAEv2-S-BIT | #8 | F1-Score: 0.9702 |
| change-detection-for-remote-sensing-images-on | RSP-ViTAEv2-S-BIT | #9 | F1-Score: 0.9681 |
| change-detection-for-remote-sensing-images-on | RSP-ResNet-50-BIT | #11 | F1-Score: 0.96 |
| change-detection-for-remote-sensing-images-on | RSP-Swin-T-BIT | #13 | F1-Score: 0.9521 |
| object-detection-in-aerial-images-on-dota-1 | RSP-ViTAEv2-S-FPN-ORCN | #29 | mAP: 77.72% |
| object-detection-in-aerial-images-on-dota-1 | IMP-ViTAEv2-S-FPN-ORCN | #32 | mAP: 77.38% |
| object-detection-in-aerial-images-on-dota-1 | RSP-ResNet-50-FPN-ORCN | #38 | mAP: 76.50% |
| object-detection-in-aerial-images-on-dota-1 | RSP-Swin-T-FPN-ORCN | #40 | mAP: 76.12% |
| object-detection-in-aerial-images-on-hrsc2016 | RSP-ViTAEv2-S-FPN-ORCN | #5 | mAP-07: 90.4 |
| object-detection-in-aerial-images-on-hrsc2016 | IMP-ViTAEv2-S-FPN-ORCN | #6 | mAP-07: 90.4 |
| object-detection-in-aerial-images-on-hrsc2016 | RSP-ResNet-50-FPN-ORCN | #7 | mAP-07: 90.3 |
| object-detection-in-aerial-images-on-hrsc2016 | RSP-Swin-T-FPN-ORCN | #8 | mAP-07: 90.0 |
| semantic-segmentation-on-isaid | IMP-ViTAEv2-S-UperNet | #11 | mIoU: 65.3 |
| semantic-segmentation-on-isaid | RSP-ViTAEv2-S-UperNet | #14 | mIoU: 64.3 |
| semantic-segmentation-on-isaid | RSP-Swin-T-UperNet | #15 | mIoU: 64.1 |
| semantic-segmentation-on-isaid | RSP-ResNet-50-UperNet | #18 | mIoU: 61.6 |
| semantic-segmentation-on-isprs-potsdam | IMP-ViTAEv2-S-UperNet | #5 | Overall Accuracy: 91.6 |
| semantic-segmentation-on-isprs-potsdam | RSP-ViTAEv2-S-UperNet | #10 | Overall Accuracy: 91.21 |
| semantic-segmentation-on-isprs-potsdam | RSP-Swin-T-UperNet | #12 | Overall Accuracy: 90.78 |
| semantic-segmentation-on-isprs-potsdam | RSP-ResNet-50-UperNet | #14 | Overall Accuracy: 90.61 |