 This paper proposes a deep learning-based framework for detecting and counting oil palm trees from high-resolution remote-sensing images. The authors trained a CNN model on manually interpreted samples and then applied it to the entire dataset. This resulted in a 96% accuracy rate, which was significantly higher than the other four methods tested. This article was authored by Wei Jili, Huang Fu, Liu, and others.