## 3 Experimental Setup
### Baseline Methods and Metrics  
The following four state-of-the-art (SOTA) and mainstream BE and CD networks were compared with the proposed approach.  
* **UNet for BE.** A standard ResNeXt101-based UNet, which is a typical network for semantic segmentation tasks, is used as a BE baseline method [12].
* **Foreground-Aware Relation Network (FarSeg) for BE.** The encoder of FarSeg consists of a ResNet-based backbone to produce pyramidal feature maps with a strategy similar to that of the feature pyramid network (FPN) and a foreground-scene relation module to improve the embeddings with associating geospatial scene-relevant context. The decoder recovers the spatial size of the relation-enhanced multi-scale feature maps. The foreground-scene relation sub-network refines each level of the pyramidal feature maps using a relation map, a similarity matrix calculated with the scene embedding and the foreground representation. The geospatial scene embedding, a 1-D feature vector, was produced by an additional branch in the backbone via global context aggregation, and the foreground represents the multi-scale features.
* **Siam-UNet and Siam-UNet++ for CD.** Siamese-UNet is mainly composed by two main parts: the siamese network for feature extraction from multi-temporal inputs and the decoder for analyzing embedding differences [25]. In the feature extraction step, images of two different periods are processed by the two branches within the siamese network with shared weights. Siam-UNet ++ uses UNet++ as the backbone network, with the advantages of capturing fine-grained details by exploiting multiple nested and dense skip connections to obtain multi-scale features and reduce the pseudo-changes induced by scale variances [22].  
* **ChangeStar for single task CD and MTL of BE&CD**. ChangeStar [40] consists of a dense prediction model for feature extraction and a ChangeMixin module to detect object change. The ChangeMixin module consists of a temporal swap module (TSM) and a shallow FCN involving combinations of Convolution-BatchNorm-ReLU. The TSM module takes bi-temporal feature maps from the backbone (FarSeg) as input, which are then concatenated in the channel axis in two different temporal orders; the two respective outputs from TSM are subsequently used as inputs to two FCNs with shared weights. Notably, the ChangeStar can be constructed for simultaneous BE and CD if a regression module is introduced for BE probability estimation using the extracted semantic features. Here, we use the FarSeg as the semantic segmentation model, which helps achieve the best CD result in [40].  
All of these baselines employ a ResNeXt101-based backbone for a fair comparison. For the same reason, all models in this study ends with a upsampling layer with a ratio of four to recover the resolution of the prediction.  
To be consistent with previous studies, we assess different approaches via four metrics: IoU, precision, recall, and _F1_-score, with respect to the building and the changed category. We do not use mean IoU for model evaluation as the number of different classes is very unbalanced, and usually the building and the changed pixels are of interest.  
The challenge uses a pixel-level evaluation metric, the _F1_ score, which is widely used and calculated using the following equation:  
\[F_{1}=\frac{2\times P\times R}{P+R}.\]  
\(P\) (precision) and \(R\) (recall) are calculated using the following two equations:  
\[P=\frac{TP}{TP+FP}.\]  
\[R=\frac{TP}{TP+FN},\]  
in which _TP_ are true positives, _FP_ are false positives, _FN_ are false negatives. Assuming that _F1_before_ and _F1_after_ represent the scores of the BE results of the paired images, and _F1_change_ represents the CD results, the final score for the Gaofen Challenge is calculated by \(Score=0.2\times(F_{1\_before}+F_{1\_after})+0.6\times F_{1\_change}\).