Ayshi, Israt JahanHaque, MaishaAnis, Sadaf M.Tasnim, NahianMoontaha, MithilaAhmed, Md. SabbirHossain, Muhammad Iqbal2026-10-012026-10-012024-01-01I. J. Ayshi et al., "Comparative Analysis of Deep Learning and OBIA on Satellite Images for Forest Cover Monitoring," 2024 27th International Conference on Computer and Information Technology (ICCIT), Cox's Bazar, Bangladesh, 2024, pp. 599-603, doi: 10.1109/ICCIT64611.2024.11022550.97983315190942-s2.0-105009053275https://hdl.handle.net/10361/30347This study aims to compare forest cover mapping techniques using Object-Based Image Analysis (OBIA) and Deep Learning (DL) models, leveraging the advancements in satellite imagery for accurate forest monitoring. The study implements state-of-the-art deep learning models - UNet, UNet++, and ResNet50-SegNet on high-resolution Sentinel-2 Level 2A satellite images of the Amazon Rainforest. Two primary datasets of Amazon Rainforest images were gathered consist of 3-band and 4-band images. After assessing the performance of these models, the paper contrasts the efficacy of deep learning methods with the traditional OBIA approach. While the traditional OBIA approach reached 92.91%, the deep learning models' highest overall accuracy are- 94.15% for UNet, 94.25% for UNet++, and 94.27% for ResNet50-SegNet. Furthermore, this research contributes to the possibilities of free accessible software like QGIS to achieve better mapping accuracy of forest cover within their limitations, to support global environmental monitoring and management.599-603en-USDeep learningRainforestsAnalytical modelsAccuracyImage analysisForestrySoftwareSatellite imagesEnvironmental monitoringInformation technologyForest CoverForest management.Forests and forestry--Remote sensing.Conference Proceeding10.1109/ICCIT64611.2024.11022550