Satellite based deep learning approaches for detecting environmental disasters across Kazakhstan

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. Deep learning significantly improves the effectiveness of satellite-based detection of floods and wildfires, ensuring high-accuracy monitoring across Kazakhstan’s diverse landscapes. Multisensor data fusion (e.g., optical and SAR) allows researchers to bypass the limitation of cloud cover, providing reliable 24/7 disaster monitoring and continuous early warning system functionality. The development of localized AI models is critically important for better adaptation to specific regional challenges, including the scarcity of labeled data and the unique climatic conditions of Central Asia.