# Semantic-Enhanced Foundation Model for Coastal Land Use Recognition from Optical Satellite Images
###### Abstract
The performance of cutting-edge deep learning models relies heavily on a great number of well-labeled datasets [14; 15]. This means that optical satellite images for different coastal land uses that vary in relation to diverse land covers should be specifically collected. Moreover, the scale of similar coastal land covers might vary significantly in the optical satellite image. For example, San Francisco might be generically defined as downtown and commercial areas at different scales. Thus, building a large-scale benchmark dataset and fine tuning the deep learning models would be time consuming, costly, burdensome, and computationally expensive. Considering the complexity of coastal land uses, it is impossible to create a benchmark dataset that fully covers all types of coastal land uses. In addition to the challenges of using state-of-the-art deep learning, the difference between coastal land cover and coastal land use is another concern. The state-of-the-art approaches mainly represent physical land cover forms in coastal areas. However, as mentioned above, research on coastal land use focuses not only on the land cover conditions but also on the interaction between natural elements and human activities. Land cover attributes alone might be insufficient for a prediction of the functionality and organization of different land parcels. Figure 1 uses an example to explain why the objective of coastal land use recognition is different from the one of land cover classification. All satellite images are from a state-of-the-art benchmark dataset regarding land use [16; 17; 18]. In these benchmark datasets, all images are viewed as similar categories, such as coast, seashore, seaport, etc. This means that the deep learning models pretrained and fine-tuned with these benchmark datasets would identify that all five satellite images at the bottom are similar to the top one. This result might be correct in the evaluation of land cover classification. Incase of coastal land use recognition, however, the results of these six satellite images are different. Thus, specific investigations should be given to support coastal land use recognition. Several researchers have reported their efforts to raise the transferability of high-level features generated by foundation models to jump the load of creating a massive amount of labeled data [19; 20]. These foundation models promote the development of weakly supervised and rule-enhanced deep learning algorithms. Motivated by these ideas, this paper proposes an integrated framework to support complex coastal land use recognition, including the coastal land cover classification by the deep learning-powered multi-label recognition with spatial weights, and the conversion from coastal land cover mappings into coastal land use semantics with the vector space model (VSM). The remainder of this paper is organized as follows. Section 2 illustrates the architecture of the proposed methodology. Sections 3 and 4 present the details of multi-label land cover classification with foundation modal and semantically aware land use recognition. Section 5 reports the experimental results using the coastlines in California as the study area, while Section 5 summarizes the contributions of this paper and provides related perspectives.