A transfer learning-based method for water body segmentation in remote sensing imagery: a case study of the Zhada Tulin area

The Tibetan Plateau, known as the ‘Asian Water Tower’, faces significant water security challenges due to its high sensitivity to climate change. Advancing Earth observation for sustainable water monitoring is thus essential for building climate resilience in this region. This study proposes a two-stage transfer learning strategy using the SegFormer model to overcome domain shift and data scarcity – key barriers in developing robust AI for climate-sensitive applications. After pre-training on a diverse source domain, our model was fine-tuned for the arid Zhada Tulin area. Experimental results show a substantial performance boost: the Intersection over Union (IoU) for water body segmentation surged from 25.50% (direct transfer) to 64.84%. This AI-driven accuracy is crucial for disaster risk reduction, particularly in monitoring flash flood-prone systems. Beyond quantitative improvements, our analysis demonstrates that domain-specific transfer learning enhances the model’s detection capability. Specifically, it achieves high recall rates (>80%) while maintaining robust precision (>73%) – a balance critical for risk monitoring applications. The resulting high-precision maps provide valuable support for understanding hydrological patterns and informing water management strategies in this climatically sensitive region. Our work thus demonstrates an effective technical solution for monitoring arid plateau regions and contributes to advancing AI-powered Earth observation for disaster preparedness in critical transboundary river headwaters.