ResNet–U-Net Based Convolutional Neural Network for Detecting Deforestation Caused by Oil Palm Plantation Expansion Using Satellite Imagery

Abstract

Deforestation caused by oil palm plantation expansion has become one of the most significant environmental issues in tropical countries, particularly Indonesia. Continuous land conversion threatens biodiversity, accelerates carbon emissions, and reduces forest ecosystem sustainability. Conventional monitoring methods are often constrained by limited spatial coverage, high operational costs, and time-consuming field surveys. This study proposes a deep learning framework that integrates ResNet-50 and U-Net architectures within a Convolutional Neural Network (CNN) for semantic segmentation of satellite imagery to detect deforestation caused by oil palm expansion. The proposed framework employs ResNet-50 as the encoder for extracting high-level spatial features, while U-Net performs pixel-level segmentation through an encoder-decoder architecture with skip connections.