Satellite based deep learning approaches for detecting environmental disasters across Kazakhstan

Nurtas M. Nurakynov S. Altaibek A. Mergembayeva A. Mohammed M.A.
February 2026 Springer Nature

Discover Applied Sciences
2026 #8 Issue 2

This review synthesizes recent advances in deep learning and satellite remote sensing for environmental disaster detection, with a specific focus on Kazakhstan. Drawing from 107 peer-reviewed studies (2018–mid-2025) identified through Scopus, Web of Science, and IEEE Xplore, we analyze DL applications across five major hazards: floods, wildfires, oil spills, drought, and land degradation using optical and synthetic aperture radar (SAR) imagery. Key architectures include convolutional neural networks, U-Net variants, and multi-modal sensor fusion models, with reported performance gains such as up to 18% improvement in mean intersection-over-union via SAR-optical fusion. We highlight Kazakhstan-specific challenges, including snow-water spectral confusion in the Normalized Difference Water Index (NDWI), NDVI saturation in steppe environments, and acute scarcity of locally labeled training data. Using high-resolution imagery from the national KazEOSat-1 system and Sentinel missions, we illustrate gaps in regional model adaptation through case studies of the 2024 Ural River floods and Aral Sea desertification. A comparative framework of data sources, models, and metrics is proposed to guide localized hazard analysis. We outline practical future directions including cross-regional transfer learning, multimodal SAR–optical fusion, and cloud-native processing pipelines tailored to Central Asia.

AI monitoring , Central Asia , Deep learning , Disaster detection , Kazakhstan , Remote sensing , Transfer learning

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Institute of Ionosphere, Almaty, 050020, Kazakhstan
Geology Department, Faculty of Science, Sohag University, Sohag, 82524, Egypt
Department of Mathematical and Computer Modeling, International Information Technology University, Almaty, 050040, Kazakhstan

Institute of Ionosphere
Geology Department
Department of Mathematical and Computer Modeling

10 лет помогаем публиковать статьи Международный издатель

Книга Публикация научной статьи Волощук 2026 Book Publication of a scientific article 2026