In recent years, the exponential growth of Earth observation data has posed significant challenges and opportunities for geospatial analysis and environmental monitoring. Traditional methods of processing and analysing this data often fall short in handling the sheer volume and complexity involved. This has led to the emergence of innovative tools and platforms like ArcGIS platform, Google Earth Engine, and Open Data Cube, which are designed to leverage advanced machine learning techniques and scalable data infrastructures. These platforms not only facilitate the storage and access of vast datasets but also enable sophisticated analysis that was previously unattainable. By integrating machine learning capabilities, these tools can be used in robust solutions for complex environmental issues, allowing accurate and timely insights into phenomena such as climate change, deforestation, urbanisation, and notably, shoreline extraction.
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