Highlights What is the main finding? The proposed Evolutionary Feature Gaussian Process (EF-GP) can effectively identify anomalous observations arising from deviations in glacial lake boundary recognition caused by the interference of clouds, snow, and terrain shadows in remote sensing imagery, while preserving the genuine evolutionary processes of the glacial lakes. What is the implication of the main finding? The 'EF-GP' proposed in this study substantially enhances the quality of automated remote-sensing mapping of glacial lakes, enabling the development of high-quality, long-term glacial-lake datasets and providing reliable support for large-scale monitoring.Highlights What is the main finding? The proposed Evolutionary Feature Gaussian Process (EF-GP) can effectively identify anomalous observations arising from deviations in glacial lake boundary recognition caused by the interference of clouds, snow, and terrain shadows in remote sensing imagery, while preserving the genuine evolutionary processes of the glacial lakes. What is the implication of the main finding? The 'EF-GP' proposed in this study substantially enhances the quality of automated remote-sensing mapping of glacial lakes, enabling the development of high-quality, long-term glacial-lake datasets and providing reliable support for large-scale monitoring.Abstract The retreat of glaciers has accelerated the expansion of glacial lakes, heightening the risk of outburst floods. Satellite remote sensing provides a crucial means for monitoring these lakes. Yet, artifacts caused by cloud cover and shadows inevitably persist even after preprocessing, compromising the reliability of large-scale automated analyses. However, the conventional approach views such data noise merely as an obstacle to be removed. The critical research gap lies in the lack of systematic methods to identify and filter out anomalies arising from unavoidable interferences actively. To address this, we propose a Gaussian process anomaly detection method that incorporates features of glacial lake evolution. By modeling how lakes change over time and establishing confidence intervals, this study effectively detects anomalies in automatically identified glacial lakes from remote sensing imagery. Analysis of typical Himalayan glacial lakes demonstrates that this method achieves an F1-score of 0.95, significantly improving the precision of remote sensing datasets. Overall, this research provides valuable technical support for developing high-quality glacial lake datasets and for automating lake monitoring.