aspg-3-3841-1791417723 20261008000203 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Fusion: Practice and Applications FPA 2770-0070 2692-4048 2025 20 1 Ayat Jasim Mohammed Al-Amarah University College Department: Medical instrumentation Techniques, Iraq Ali Raheem Khraibet Imam AL-Kadhum College (LKC) Department: Computer techniques engineering, Iraq Huda Lafta Majeed Computer Science and Information Technology, University of Wasit, Al Kut 52001, Iraq Oday Ali Hassen College of Computer Science and Information Technology, Wasit University, Wasit 52001, Iraq; Ministry of Education, Wasit Education Directorate, Iraq 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 166 178 3841 https://creativecommons.org/licenses/by/4.0/ 10.54216/FPA.200113 https://www.americaspg.com/journal/3/article/3841 https://www.americaspg.com/storage/41746875617.pdf