Transfer Learning To Detect Natural, Monoculture, and Agroforestry Tree-Based Systems in Ghana Using Remote Sensing

This paper discusses a transfer learning approach to distinguish natural forests from agricultural tree systems in Ghana. By repurposing spatial embeddings from an existing convolutional neural network, the study improves tree system classification accuracy to support ecosystem service assessment, deforestation monitoring, and national restoration reporting.