# Semantic-Enhanced Foundation Model for Coastal Land Use Recognition from Optical Satellite Images
###### Abstract  
Coastal land use represents the combination of various land cover forms in a coastal area, which helps us understand the historical events, current conditions, and future progress of a coastal area. Currently, the emergence of high-resolution optical satellite images significantly extends the scope of coastal land cover recognition, and deep learning models provide a significant possibility of extracting high-level abstract features from an optical satellite image to characterize complicated coastal land covers. However, recognition systems for labeling are always defined differently for specific departments, organizations, and institutes. Moreover, considering the complexity of coastal land uses, it is impossible to create a benchmark dataset that fully covers all types of coastal land uses. To improve the transferability of high-level features generated by deep learning to reduce the burden of creating a massive amount of labeled data, this paper proposes an integrated framework to support semantically enriched coastal land use recognition, including foundation model-powered multi-label coastal land cover classification and conversion from coastal land cover mapping into coastal land use semantics with a vector space model (VSM). The experimental results prove that the proposed method outperformed the state-of-the-art deep learning approaches in complex coastal land use recognition. t 2024 1 2024  
and natural activities. This cycle of human interventions makes coastal land use within multiple land cover forms much more complex. Traditional field investigations, which are generally laborious and time consuming, might be inefficient for a study on coastal land uses in a large-scale area. Frequently updated optical satellite images provide great potential to support large-scale coastal land use recognition and reinforce researchers' competence in understanding, estimating, and predicting the influences of natural forces and artificial activities. Early remote sensing-based coastal land use recognition mainly relied on medium-resolution satellite images [1; 2]. Details of land use and land uses in coastal areas have not been available to be represented in these optical satellite images due to the limitations of spatial resolution. Currently, the emergence of high-resolution optical satellite images significantly extends the scope of coastal land use recognition, and a variety of approaches in terms of coastal land cover recognition have been proposed, such as image processing techniques [3; 4], spatio-contextual approaches [5], spatial-temporal analysis [6], machine learning-based approaches [7; 8], and rule-based approaches [9; 10; 11]. In the case of coastal land use recognition, those approaches might require a great amount of trial-and-error effort thresholds, such as setting a representative scale of segmentation and selecting representative training samples and features. Robustness is another major concern because these approaches have restrictions in dealing with noises and variously complicated patterns of coastal land covers. Finally, and most importantly, traditional machine learning approaches are sufficient for handling the hyper-dimensional data feature space derived from massive optical satellite images [12]. The rapid progress of deep learning such as CNNs, ViT, etc., provides a great possibility of extracting high-level abstract features from an optical satellite image to characterize complicated coastal land uses. The strength of deep learning models in feature extraction and feature learning has attracted considerable attention from the land use mapping community, leading to a great number of various deep learning models implemented in processing optical satellite images [13]. Although previous works using deep learning models have demonstrated a powerful capability to perform coastal land use recognition, the availability of well-labeled datasets is the critical challenge that remains unexplored for efficient coastal research based on optical satellite images.