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Table 1 The perception results of our proposed frameworks: where AR indicates the percentage before and after the average area change. PQ indicates the parameter quantification of the methods. Time indicates the run time of each batch size.“ –” means equal values for the same model

From: Exploring spatiotemporal changes in cities and villages through remote sensing using multibranch networks

Datasets Model P(%) R(%) F(%) AP(%) PQ(M) Time(s)
LEVIR-CD VGG-LR 63.54 65.19 64.35 12.33 4.12 2.01
ChangeNet 64.98 67.56 66.24 13.08 6.19 3.81
FDCNN 67.49 68.95 68.21 14.59 6.01 4.92
CD-UNet++ 68.55 70.07 69.30 15.38 5.54 4.53
UNetLSTM 69.28 71.05 70.15 17.18 12.01 7.32
SRCDNet 74.83 80.62 77.62 20.59 10.11 6.07
ESCNet 77.07 84.16 80.45 23.44 17.22 11.88
FGCN [29] 78.51 79.44 78.97 24.94 19.72 14.43
PToP CNN [26] 79.92 81.57 80.74 26.26 20.02 15.84
KPCAMNet 81.56 86.07 83.75 27.48 21.49 16.09
Ours 84.38 92.04 88.04 31.43 18.14 11.95
SZAB VGG-LR 33.24 35.52 34.34 5.19
ChangeNet 34.28 37.16 35.66 5.86
FDCNN 35.12 38.05 36.52 6.27
CD-UNet++ 36.94 38.99 37.93 6.88
UNetLSTM 37.52 39.65 38.56 7.22
SRCDNet 40.69 42.28 41.47 9.44
ESCNet 42.14 44.37 43.23 10.87
FGCN [29] 43.49 45.74 44.57 11.04
PToP CNN [26] 44.23 46.07 45.13 11.26
KPCAMNet 44.52 46.01 45.25 11.32
Ours 46.15 64.27 53.72 13.49