## 3 Results
### Scenario I
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Refuge** & **Season** & **Training** & **Baseline** & **Scenario I** & \multicolumn{4}{c}{**Scenario II**} \\ \cline{3-10}
**Camp** & **Sears** & **Manual** & **OBIA** & **10\%** & **20\%** & **30\%** & **40\%** & **50\%** \\ \hline \multirow{4}{*}{Kule} & \multirow{4}{*}{Dry} & OCOS & 0.6840 & 0.5912 & 0.7370 & 0.7313 & 0.7335 & 0.7299 & 0.7377 \\  & & OCTS & 0.6910 & 0.5790 & 0.6798 & 0.7066 & 0.7071 & 0.7180 & 0.7135 \\  & & TCOS & 0.6940 & 0.5879 & 0.7002 & 0.7035 & 0.7076 & 0.7135 & 0.7180 \\  & & TCTS & 0.6959 & 0.5950 & 0.6553 & 0.6912 & 0.6922 & 0.7007 & 0.6960 \\ \hline \multirow{4}{*}{Kule} & \multirow{4}{*}{Wet} & OCOS & 0.7116 & 0.6162 & 0.7470 & 0.7551 & 0.7324 & 0.7324 & 0.7349 \\  & & OCTS & 0.7245 & 0.6005 & 0.6766 & 0.7120 & 0.7225 & 0.7228 & 0.7298 \\  & & TCOS & 0.7148 & 0.5755 & 0.7209 & 0.7243 & 0.7307 & 0.7422 & 0.7377 \\  & & TCTS & 0.7124 & 0.6234 & 0.6966 & 0.7051 & 0.7338 & 0.7281 & 0.7249 \\ \hline \multirow{4}{*}{Nguenyygiel} & \multirow{4}{*}{Dry} & OCOS & 0.7760 & 0.7093 & 0.7536 & 0.7628 & 0.7623 & 0.7668 & 0.7679 \\  & & OCTS & 0.7657 & 0.7110 & 0.7493 & 0.7552 & 0.7683 & 0.7635 & 0.7651 \\ \cline{1-1}  & & TCOS & 0.7698 & 0.7043 & 0.7569 & 0.7667 & 0.7688 & 0.7677 \\ \cline{1-1}  & & TCTS & 0.7713 & 0.7186 & 0.7562 & 0.7779 & 0.7782 & 0.7825 & 0.7784 \\ \hline \hline \multirow{4}{*}{Nguenyygiel} & \multirow{4}{*}{Wet} & OCOS & 0.7553 & 0.7042 & 0.7474 & 0.7595 & 0.7694 & 0.7628 & 0.7553 \\ \cline{1-1}  & & OCTS & 0.7608 & 0.7045 & 0.7487 & 0.7592 & 0.7621 & 0.7595 & 0.7552 \\ \cline{1-1}  & & TCOS & 0.7638 & 0.6890 & 0.7627 & 0.7712 & 0.7717 & 0.7724 & 0.7747 \\ \cline{1-1}  & & TCTS & 0.7712 & 0.7138 & 0.7245 & 0.7626 & 0.7754 & 0.7667 & 0.7703 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The Recall values of built-up structures of all implemented experiments. values (within 0.01, in general) brought by the three intermixing strategies are insignificant for the Manual labels. For the OBIA labels, the TCTS intermixing strategy shows a stable but limited improvement (approximately 0.01). The other two intermixing strategies cannot consistently improve the model performance. Instead, they decrease the IoU values in most cases. The Recall values share similar findings as the IoU values, as shown in Table 3. We can observe the TCTS intermixing strategy brings a consistent improvement (approximately 0.01) for the models trained with the OBIA labels, but the other two strategies harm the model performance in most cases. For Precision values as shown in Table 4, we can observe that the OBIA labels outperform the Manual labels in Precision values in all implemented experiments. On the contrary, the Manual labels outperform the OBIA labels in terms of Recall. For instance, in the [PERSON] case, the Precision values of the OBIA-OCOS model and the Manual-OCOS model are 0.8545 and 0.8147, respectively. However, the Recall values of the same two models are 0.6890 and 0.7638, respectively. This finding indicates that the annotation errors in the OBIA labels cause models to miss much of TP in prediction. In the following content, we used Kule Wet as an example to explain the above findings because Kule Wet includes the most typical examples for an explanation. Figure 8 displays the spatial distribution of predicted FP (blue) and FN (red) pixels of the OBIA-OCOS model (left) and the Manual-OCOS model (right). Overall, we can observe the two models share a similar spatial distribution of FP and FN pixels, but the OBIA-OCOS model produced more falsely predicted pixels. We selected five typical examples (A)-(E) to further show the prediction differences between the two models, as shown in Figure 9. In general, we found four main sources of errors coming from facility buildings, trees, boundaries, and brown cloth on the rooftop of dwellings. There are two main types of facility buildings in the selected camps. Most facility buildings are white and grey in terms of spectral characteristics. Only a few of them are brownish.