# Semantic-Enhanced Foundation Model for Coastal Land Use Recognition from Optical Satellite Images
## 6 Conclusions  
The availability of deep learning models and varied high-resolution optical satellite images significantly facilitates the exploration of mapping coastal land covers within large-scale areas. However, the land cover classification map cannot help individuals understand an area's functionality and organization within multiple land use categories. The conversion from a land cover classification map to land uses remains the major geospatial concern yet unsolved. A majority of the previous works implemented the deep learning models into a study case, for which the amount and term of land cover forms were fixed and generally defined. Here, when we focused on the coastal land use possessing a greater land use definition and hypothesis details, the state-of-the-art deep learning models were faced with a decline in classification accuracy. Specifically, it was impossible to prepare massive training data when the distinction between two land uses was very narrow.  
The cost of large-scale data preparation urges researchers to reinforce the power of deep learning models in land use recognition with a limited amount of data. This paper presents a methodology that combines a semantic-enriched approach and foundation model. The proposed method integrates the domain knowledge into the process of feature learning for converting land cover forms into meaningful land-use information. We evaluate the proposed model after choosing the coastal land uses in California as the study area. The results support the hypothesis that the deep learning models might be insufficient for land cover recognition with small-scale training datasets, and the foundation model might fail to support land use recognition without support from domain knowledge.  
In the future, it may be valuable to explore how the semantics may be integrated into the features generated by the feature extraction layer in a deep learning or foundation model. Furthermore, the techniques associated with transfer learning and reinforcement learning are fields worthy of considerable attention. Strategies for recognizing these unknown land covers are a new topic worthy of future investigation.  
Conceptualization, X.Z.; methodology, M.S.; validation, M.S. and X.X.; investigation, K.L. and C.L.; data curation, K.L.; writing--original draft preparation, M.S. and X.Z.; writing--review and editing, X.X. and C.L.; project administration, X.Z. and X.X.; funding acquisition, X.Z. and X.X. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant number 41971370, State Key Laboratory of Geo-Information Engineering and Key Laboratory of Surveying and Mapping Science and Geospatial Information Technology of MNR, CASM grant number 2024-04-13, Key Research and Development Program of Ningxia Hui Autonomous Region grant number 2023 BEG02068, Beijing Nova Program grant number 20230484351, Huzhou Key Research and Development Program grant number 2023 ZD2046.  
Not applicable.  
The data that support the findings of this study are openly available in the link of Baidu Wanpan: [[https://pan.baidu.com/s/11](https://pan.baidu.com/s/11) LkCdTaEsxuCQ2 zE4_CO_A]([https://pan.baidu.com/s/11](https://pan.baidu.com/s/11) LkCdTaEsxuCQ2 zE4_CO_A), with the password: 2 gxb (accessed on 20 September 2024).  
The authors declare no conflicts of interest.