Abstract: Infrastructure development is a leading driver of forest cover loss in the tropics, resulting in a significant decrease in biodiversity. With recent advancements in digital image processing and satellite imagery techniques, this study presents an approach for tracking forest cover alterations along a recently constructed highway in Brunei. Specifically, this paper harnesses the power of cutting-edge deep learning (DL) models; U-Net and Deeplabv3+, to classify forest and non-forest features from Sentinel-2 satellite images captured along the Telisai–Lumut highway between 2018 and 2022. A computational performance analysis and network comparison revealed that the Deeplabv3+ had a remarkable semantic segmentation performance, with a mean intersection over union of 0.9247, followed by an ensemble of four networks, with a mean intersection over union of 0.9171. The best model consistently outperformed other models in segmenting forest and non-forest features, with an intersection over union (IOU) of 0.9463 and 0.9031, respectively. Moreover, deploying hyperparameter optimization allowed us to assess the model's sensitivity to different hyperparameters for better results. Furthermore, forest change statistics were computed using the best model's generated forest change maps, and the results indicated a 16.7% gain in the overall forest vegetation cover along the highway, demonstrating the validity of the results. These reproducible findings will provide forest managers and ecologists with near real-time information on the status, health, and extent of forest ecosystems. This study's results have critical implications for using DL to monitor forest cover changes and will be of great interest to the scientific community as they open new avenues for future research and underline the need for continued innovation in this important field.