# Semantic-Enhanced Foundation Model for Coastal Land Use Recognition from Optical Satellite Images
## 5 Experiment
### Coastal Land Use Retrieval
Otherwise, although multi-class classification reached a relatively higher precision and recall on the ninth image, the classification strategy only recognized these images' content as freeway rather than other land covers. The results generated by binary classification and multi-class classification proved that merely one land cover class was not competitive enough to characterize the complicated coastal land covers in an optical satellite image. Being developed on large-scale labeled datasets, foundation models could effectively discover the high-level abstracts on land cover forms, which might indicate the significance of conducting land use and land use recognition from high-resolution optical satellite images. Multi-label classification and the proposed method all reached the top five classes rather than the top class selected by multi-class classification. From the results of Table 4, multi-label classification significantly outperformed the above two classification strategies in retrieving the needed images. However, PNASNet's performances for multi-label classification were much poorer than those previously reported--a difference that might have been caused because of two major reasons. First, the diversities of land covers were tiny among the collected images, which required massive labeled datasets for PNASNet to understand these diversities. For example, the first, ninth, tenth, and eleventh images were generally labeled as the same land use category in the existing benchmark dataset. If we grouped the images relevant to these four needed images into one land use category, the precision of multi-label classification could reach around 90 \(\pm\) 3%. Moreover, some land covers occupied a small portion of the whole image, making it difficult for them to be recognized by PNASNet with a limited amount of labeled datasets. The proposed method outperformed multi-label classification, thus confirming the value of an operation that weighted different features based on semantics. Moreover, the disparities in results generated by these two classification strategies varied significantly based on different land use categories. Generally speaking, the proposed method performed more effectively on the images that contained more complicated land use configurations. Finally, but certainly not least, the process of semantic analysis in the proposed method might have led to unwanted complexity when the coastal land cover in an optical satellite image was unmixed. For example, the proposed image-semantic model produced many false-negative results due to its overestimating the role of other non-freeway land covers. In these images, although freeway was the critical element of the land-use characteristics, its coverage seemed relatively more restricted compared to other land covers. The results generated by the proposed method presented several new phenomena and challenges that remain unsolved. The results exhibited the major disadvantage of the deep learning models. They always heavily relied on the training data, which intensely restricted these models' transferability. In the experiment, when fusing the harbor and wharf into one land use category, we saw a dramatical decrease in the precision of retrieving images relevant to the second and third images. Moreover, the results generated by multi-label classification pose a pressing demand on the integration of semantics into the foundation model-enhanced land use recognition.