Mohd Asyraf ZulkifleyFung Xin Ru2024-04-152024-04-152022Ru, F. X., Zulkifley, M. A., Abdani, S. R., & Spraggon, M. (2023). Forest Segmentation with Spatial Pyramid Pooling Modules: A Surveillance System Based on Satellite Images. Forests, 14(2), 405.https://openscience.ukm.my/handle/123456789/55The global deforestation rate continues to worsen each year and it is essential to develop an effective forest monitoring system to detect any changes in forest areas. In general, changes in forest status are difficult to annotate manually, whereby the boundaries can be small in size or hard to discern, especially in areas that are bordering residential areas. Therefore, the goal of this study is to overcome these issues by developing a forest monitoring system that relies on a robust deep semantic segmentation network that is capable of discerning forest boundaries automatically, so that any changes over the years can be tracked. The backbone of this system is based on satellite imaging supplied to a modified U-Net deep architecture to incorporate multi-scale modules to deliver the semantic segmentation output. A dataset of 6048 Landsat-8 satellite sub-images that were taken from eight land parcels of forest areas was collected and annotated, and then further divided into training and testing datasets. The novelty of this system is the optimal integration of the spatial pyramid pooling (SPP) mechanism into the base model. The results demonstrated the effectiveness of the SPP module in improving the performance of the forest segmentation system by 2.57%, 6.74%, and 7.75% in accuracy, intersection over union, and F1-score, respectively. As a result, the multi-scale module improved the proposed forest segmentation system, making it a highly useful system for government and private agencies in tracking any changes in forest areas.Automated Forest MonitoringMachine LearningDeep LearningConvolutional Neural NetworkSemantic Segmentation