Land cover refers to the physical material present on the surface of the Earth. Land cover maps are valuable tools in urban planning, agriculture, resource management, change detection, and various other applications that require accurate information about the Earth’s surface. However, land-cover classification is a complex task that is often challenging to achieve with traditional methods.

While public land cover datasets are available, they may have limitations in terms of spatial or temporal resolution and might not align with specific user needs. Creating custom datasets for a particular time period also demands significant time and expertise.

This deep learning model offers an efficient solution by automating the land cover classification process, greatly reducing the manual effort and time required while delivering high-quality results.