<p>Advancements in image processing and integration of machine learning and deep learning algorithms with satellite imagery have led to automated feature extraction within DIP software (ENVI). These software packages now incorporate robust algorithms and tools that facilitate pre-processing and digital processing of images to extract specific features or objects of interest from digital data but may not provide the same level of GIS workflows as dedicated GIS software (ArcGIS), especially when converting raster-based features or objects into vector-based formats (vectorization), such as points, lines, and polygons. Vectorization is a critical step and allows users for more precise spatial analysis and integration of features into GIS workflows. ArcGIS, on the other hand, offers a wide range of tools and capabilities for handling geographic information, satellite imagery, and geophysical data but lacks pre-processing tools for different EO sensors and does not incorporate advanced algorithms for sophisticated image processing. This limitation often necessitates users to frequently switch between DIP and GIS software for feature or shoreline extraction. While DIP software is well-suited for the initial stages of feature extraction, GIS software excels in subsequent tasks such as vectorization, boundary smoothing, topological cleaning, and spatial database generation. Consequently, the prime objective of this study is to develop a unified process for automated shoreline extraction and database generation from multiband satellite imagery, exclusively within the ArcGIS Environment. This approach is to streamline the workflow, minimize the necessity of switching between DIP and GIS software, and harness the full potential of ArcGIS’s powerful geospatial capabilities for shoreline extraction, in-depth analysis, and compelling visualization.</p>