Fusion: Practice and Applications FPA 2692-4048 2770-0070 10.54216/FPA https://www.americaspg.com/journals/show/3841 2018 2018 Al-Amarah University College Department: Medical instrumentation Techniques, Iraq Oday Oday Imam AL-Kadhum College (LKC) Department: Computer techniques engineering, Iraq Ali Raheem Khraibet Computer Science and Information Technology, University of Wasit, Al Kut 52001, Iraq Huda Lafta Majeed College of Computer Science and Information Technology, Wasit University, Wasit 52001, Iraq; Ministry of Education, Wasit Education Directorate, Iraq Oday Ali Hassen This study affords a deep autoencoder-primarily based framework for anomaly detection in multispectral satellite tv for pc imagery, addressing vital challenges in environmental monitoring and disaster response. Utilizing datasets from Sentinel-2, Landsat-eight, and MODIS, the version employs a hybrid loss function (MSE+MS-SSIM) and spatial attention mechanisms to discover and localize anomalies consisting of wildfires, floods, and urban encroachment. Experimental outcomes display superior overall performance (F1-Score: 0.84, AUC-ROC: 0.93) compared to PCA and Isolation Forest baselines, with precise anomaly localization demonstrated thru errors heatmaps and IoU metrics. The framework’s integration with early warning structures highlights its capability for actual-time applications, although boundaries in managing seasonal versions and occasional-decision information underscore the want for future paintings in multi-modal fusion and semi-supervised studying. This study advances scalable solutions for sustainable land control and emergency response, leveraging open-supply satellite data for global accessibility. 2025 2025 166 178 10.54216/FPA.200113 https://www.americaspg.com/articleinfo/3/show/3841