## 1 Introduction  
Coastal erosion is highlighted as an urgent, pressing global issue affecting approximately 80% of shorelines worldwide with erosion effects from 1 centimeter to 30 m/year, calling for shoreline monitoring and sustainable coastal zone management with early warning frameworks and forthcoming predictions for stakeholder decisions [1]. The main factors contributing to and enhancing this phenomenon are natural forces such as waves, storms, and tides, as well as human activities such as urbanization of the coastal front and the construction of water-retaining dams. Sea level rise and extreme weather events drive significant changes in shoreline dynamics, increasing the risk of erosion, inundation, and habitat loss along the coastlines. Even moderate sea level rise is directly linked to the degradation of natural defense lines like dunes and mangroves, accelerating landward shoreline retreat, particularly in low-lying deltaic settings where land subsidence impacts the problem caused by shoreline retreat even more [2,3]. Severe occurrences such as storm surges and hurricanes are directly correlated with increased sea levels, increasing wave energy and sediment transport. This interaction rapidly shapes the morphology of the coastline [4,5]. Urbanized coastlines are particularly vulnerable due to a lack of natural defense lines and buffers, which increases the likelihood of storm-induced erosion [6,7]. These phenomena cause socio-economic instability, damaging infrastructure, reducing the biodiversity in wetlands and estuaries, and harming tourism and livelihoods [8,9]. Remote sensing, in addition to predictive models and other AI software like WaterNet, has the potential to address these issues and enhance the monitoring and forecasting of shoreline changes under climate change. Proactive and adaptive approaches--such as nature-based solutions and resilient infrastructure--require access to accurate, high-resolution spatial and temporal data, especially in areas exposed to accelerated erosion and development pressures [10,11]. Traditional shoreline monitoring methods--like ground surveys and aerial photography--serve as foundational tools but often fall short in frequency, coverage, and timeliness, especially in fast-changing or remote environments [5,6,12]. These techniques are also labor-intensive, expensive, and frequently impacted by weather or terrain constraints. In contrast, satellite-based remote sensing has revolutionized coastal monitoring by enabling regular, cost-effective observations of extensive areas under open-access policies. These platforms allow the frequent acquisition of high-resolution imagery critical for detecting shoreline changes [13]. Among the available resource datasets, open-access satellite missions like Sentinel and Landsat stand out as vital resources due to the open-access data policy. The Sentinel program, launched by the European Space Agency (ESA), includes Sentinel-1 and Sentinel-2, which provide radar and optical imagery, respectively [14]. Sentinel-2 offers high-resolution optical images with frequent revisits, while Sentinel-1's synthetic aperture radar (SAR) capabilities enable data collection regardless of weather or lighting conditions. These features make Sentinel missions especially effective in capturing rapid coastal changes under dynamic environmental conditions. The Landsat program, operated jointly by USGS and NASA since 1972, provides the most extended continuous global archive of multispectral imagery [15], which is critical and informative for understanding decadal trends in shoreline dynamics and environmental change [16]. The Sentinel-2 and Landsat missions offer complementary capabilities for shoreline detection and coastal erosion analysis. Sentinel-2 contributes by providing high-spatial-resolution imagery (10 m for visible and near-infrared bands), a wide swath width (290 km), and a frequent revisit time of 5 days with both satellites (Sentinel-2A and 2B) [14].