Peng Li ^1,2 Weipeng Li ¹ Dahai Liu ^3* Chun Chen ^4,5,6* Tianhui Fan ⁷ Renguo Gu ⁷ Ario Damar ^8,9 Min H. Htet ¹⁰ Zhen Lin ^4,5,6

• ¹ Zhuhai Da Hengqin Co., Ltd, Zhuhai, China
• ² Zhuhai Da Hengqin Urban Investment Construction Co., Ltd, Zhuhai, China
• ³ Key Laboratory of Coastal Science and Integrated Management, First Institute of Oceanography, Ministry of Natural Resources, Qingdao, China
• ⁴ Island Research Center of the Ministry of Natural Resources, Pingtan, China
• ⁵ Fujian Provincial Key Laboratory of Island Conservation and Development, Pingtan, China
• ⁶ Observation and Research Station of Island and Coastal Ecosystem in the Western Taiwan Strait, MNR, Xiamen, China
• ⁷ South China University of Technology, School of Civil Engineering and Transportation, Guangzhou, China
• ⁸ Department of Aquatic Resources Management, Faculty of Fisheries and Marine Sciences, IPB University, Bogor, Indonesia
• ⁹ Center for Coastal and Marine Resources Studies, IPB University, Bogor, West Java, Indonesia
• ¹⁰ Department of Marine Engineering, Myanmar Maritime University, Thilawa, Myanmar

Automatically and accurately identifying the deformation zone of coastal slope landslides is crucial for exploring the mechanism of landslides and predicting landslide disasters.To this end, this study proposes an integrated automatic recognition method combining Image Clipping (IC), Image Information Enhancement (IE), Adaptive K-means Clustering Segmentation (AKS), and Optimization (O): IC-IE-AKS-O, which achieves precise extraction of the deformation area in coastal slope landslide images. Firstly, due to the more complex natural environment of field slopes, to extend the monitoring duration, we introduce a hierarchical operation algorithm based on the HSV color model, which effectively mitigates the impact of sunlight, rain, and foggy weather on image recognition accuracy. Secondly, this study proposes a 2D landslide image segmentation technique that combines K-means clustering with global threshold segmentation for landslide images, enabling the segmentation of small image regions with precision. Finally, we combine image in-formation enhancement technology with image segmentation technology. To verify its effectiveness, we identify a landslide image of a coastal slope in Pingtan. The method displays an average relative error of 5.20% and 5.14% in the X and Y directions, respectively. Its advantages are threefold: (1) The combination of image information enhancement and segmentation techniques can more accurately identify landslide areas that appear blurred in the image; (2) expanding the temporal dimension of coastal slope monitoring; (3) providing excellent boundary conditions and segmentation results. The practical application of this method ensures the stable and accurate operation of the coastal slope monitoring system, providing a safeguard for the sustainable development of marine safety.