# Semantic-Enhanced Foundation Model for Coastal Land Use Recognition from Optical Satellite Images
## 5 Experiment
### Coastal Land Use Retrieval  
This subsection evaluated the proposed image-semantic model based on the application of image retrieval. Image retrieval aimed to search and retrieve the needed images from a large-scale image dataset [36]. Based on the remaining 500 even-numbered images, we selected twelve as the needed images, and the rest were samples in the image database. For every needed image, we retrieved all images relevant to this needed image's content based on binary classification, multi-class classification, multi-label classification, and the proposed image-semantic model. Figure 8 displayed the 11 target images for image retrieval-based evaluation. The land uses included in these eleven images were different, and each image represented an independent class. Table 3 listed the land covers in each image. Figure 8: The selected target images for image retrieval. Table 4 illustrates the retrieval results for the selected images shown in Figure 8 with various classification strategies. The results generated by PNASNet were reported by Reference [35]. Precision and recall were expressed as the following equation:  
\[\left\{ \begin{array}{l}preccision=\frac{TP}{TP+FP}\\ recall=\frac{TP}{TP+FN}\end{array} \right. \tag{10}\]  
where \(TP\), \(FP\), \(FN\), and \(TN\) denote true positive, false positive, false negative, and true negative, respectively. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Images**} & \multicolumn{3}{c}{**Land Use**} & \multicolumn{3}{c}{**Recognizable Land Covers**} & \multicolumn{3}{c}{**Amount of GTS *} \\ \hline Image 1 & Natural sand coastline & Waters, sand & 90 & \\ Image 2 & Industrial harbor & Commercial area (or industrial area), & 20 & \\ Image 3 & Entrainment harbor & Waters, wharf & 15 & \\ Image 4 & Residential coastline without sand & Waters, meadow, forest, sparse/dense residential & 40 & \\ Image 5 & Industrial coastline & Waters, sand, meadow, forest, sparse/dense & 35 & \\ Image 6 & Residential coastline without sand & Waters, pair, sand, sparse/dense residential area & 11 & \\ Image 7 & Residential coastline with a pier & Waters, pair, sand, sparse/dense residential area & 11 & \\ Image 8 & Residential coastline without sand & Waters, sparse/dense residential area & 6 & \\  & or vegetation & (or commercial area) & & & & & & & & & & & \\ Image 9 & Natural coastline with a freeway & Waters, sand, (or meadow/forest), freeway & 56 & \\ Image 10 & Natural sand coastline & Waters, sand, meadow (or forest) & 58 & \\ Image 11 & Natural sand coastline & Waters, meadow (or forest) & 30 & \\  & without sand & & Waters, meadow (or forest) & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Land use semantics of the selected images shown in Figure 8.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Binary Classification**} & \multicolumn{3}{c}{**Multi-Class**} & \multicolumn{3}{c}{**Multi-Label**} & \multicolumn{3}{c}{**The Proposed**} \\ \hline  & **P1** & **P2** & **R1** & **R2** & **P  
Binary classification identified all images as _beach_ and _non-beach_ forms. Thus, this classification strategy produced the same precision, recall, and accuracy among all twelve selected images. Above all, the results in Table 4 acknowledged that binary classification could not support to provide the representation of the land use included in an optical satellite image. Multi-class classification has been commonly reported in previous works related to content-based image retrieval. The top class generated by PNASNet and foundation models categorized the selected eleven images into one form (e.g., beach, harbor, pier, dense residential area, freeway). Thus, the retrieval accuracy varied significantly according to the different needed images. For the first, fourth, tenth, and eleventh needed images carrying similar land use, multi-class classification could not distinguish their detailed differences.