GLAVITU: A Hybrid CNN-Transformer for Multi-Regional Glacier Mapping from Multi-Source Data

Abstract

Glacier mapping is essential for studying and monitoring the impacts of climate change. However, several challenges such as debris-covered ice and highly variable landscapes across glacierized regions worldwide complicate large-scale glacier mapping in a fully-automated manner. This work presents a novel hybrid CNN-transformer model (GlaViTU) for multi-regional glacier mapping. Our model outperforms three baseline models—SETR-B/16, ResU-Net and TransU-Net—achieving a higher mean IoU of 0.875 and demonstrates better generalization ability. The proposed model is also parameter-efficient, with approximately 10 and 3 times fewer parameters than SETR-B/16 and ResU-Net, respectively. Our results provide a solid foundation for future studies on the application of deep learning methods for global glacier mapping. To facilitate reproducibility, we have shared our data set, codebase and pretrained models on GitHub at https://github.com/konstantin-a-maslov/GlaViTU-IGARSS2023.

GLAVITU: A Hybrid CNN-Transformer for Multi-Regional Glacier Mapping from Multi-Source Data