Publications by authors named "Foster Mensah"
Article Synopsis
- State-of-the-art cloud computing platforms like Google Earth Engine (GEE) enhance the process of mapping land cover changes globally using machine learning, but high-quality training data for accurate mapping is still expensive and labor-heavy.
- To solve this, we developed a global database with nearly 2 million training units from 1984 to 2020, covering seven main and nine secondary land cover classes, using GEE and machine learning for quality and representation.
- Our database, which includes diverse datasets and reflects regional land characteristics, is useful for various fields, including land cover change studies, agriculture, forestry, hydrology, and urban development.
Rapid population and economic growth quickly degrade and deplete forest resources in many developing countries, even within protected areas. Monitoring forest cover change is critical for assessing ecosystem changes and targeting conservation efforts. Yet the most biodiverse forests on the planet are also the most difficult to monitor remotely due to their frequent cloud cover.
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