Table 2 Assessment metrics.

From: Fine-grained urban blue-green-gray landscape dataset for 36 Chinese cities based on deep learning network

Assessment metrics

Formula

Precision

\(Precision=\frac{TP}{TP+FP}\)

Recall

\(Recall=\frac{TP}{TP+FN}\)

F1-Score

\(F1=\frac{2\times Precision\times Recall}{Precision+Recall}\)

Overall accuracy

\(OA=\mathop{\sum }\limits_{i=1}^{n}\frac{{x}_{ii}}{N}\)

IoU

\(IoU=\frac{TP}{TP+FP+FN}\)

FWIoU

\(FWIoU=\frac{1}{{\sum }_{i=0}^{K}{\sum }_{j=0}^{K}{x}_{ij}}\mathop{\sum }\limits_{i=0}^{K}\frac{{\sum }_{j=0}^{K}{x}_{ij}{x}_{ii}}{{\sum }_{j=0}^{K}{x}_{ij}+{\sum }_{j=0}^{K}{x}_{ji}-{x}_{ii}}\)

  1. Where TP, TN, FP and FN represent true positive, true negative, false positive and false negative; N is the total pixel number of the image. K is the number of categories. xii represents the pixel number of the category i that was correctly classified. xij represents the pixel number of the category i that are wrongly divided into category j.