Alam, Md. Golam RabiulRahman, ShowrinRahman, SowadJawad, Md. TanvirSowad, Tazower RahmanGhosh, Krishno2026-01-182026-01-1820252025-06ID 24141255ID 24341284ID 24241165ID 21201691ID 21201809http://hdl.handle.net/10361/27442Cataloged from PDF version of thesis.Includes bibliographical references (pages 63-67).This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2025.Riverbank erosion in Bangladesh causes significant environmental and socioeconomic challenges, including land loss that displaces communities and damages local economies. Traditional monitoring methods, such as field surveys and manual satellite imagery analysis, are labor-intensive, inaccurate, and lack continuous data. Synthetic Aperture Radar (SAR) imagery from Sentinel-1 provides precise, all-weather imaging, overcoming cloud cover limitations. We created a dataset using Sentinel Hub, retrieving daily SAR data from 2014 to 2024 for major Bangladeshi rivers, including Jamuna (Sirajganj, Tangail, Manikganj), Padma(Shariatpur, Munshiganj, Faridpur), Meghna (Chandpur, Lakshmipur, Narsingdi), Brahmaputra (Chilmari, Fulchhari, Bahadurabad), and Teesta (Lalmonirhat, Kurigram). This study addresses temporal data gaps in SAR imagery using Generative Adversarial Networks (GANs) to reconstruct missing data, ensuring continuous riverbank observations. It also employs a self-supervised learning (SSL) framework with a convolutional Masked Autoencoder (MAE) and Mamba blocks for land-water segmentation without labeled data. Our pipeline achieved an average River Intersection over Union (IoU) of 0.8550 and a Dice coefficient of 0.9038 across five rivers from 2015 to 2024, enabling robust segmentation. This approach supports scalable riverbank monitoring for disaster prevention and sustainable environmental management in Bangladesh.67 pagesenBRAC University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission.Riverbank erosionSynthetic Aperture RadarSelf-supervised learningGenerative Adversarial NetworksSynthetic image generationSoil erosion--Bangladesh.Synthetic aperture radar.Learning, Psychology of.Generative adversarial networks (Computer networks).Thesis