MASSIVE – MAchine learning, Surface mass balance of glaciers, Snow cover, In-situ data, Volume change and Earth observation

• Maslov, Konstantin; Persello, Claudio; Schellenberger, Thomas & Stein, Alfred (2024). Towards Global Glacier Mapping with Deep Learning and Open Earth Observation Data. arXiv.org. ISSN 2331-8422. doi: 10.48550/arXiv.2401.15113. Show summary

  Accurate global glacier mapping is critical for understanding climate change impacts. It is challenged by glacier diversity, difficult-to-classify debris and big data processing. Here we propose Glacier-VisionTransformer-U-Net (GlaViTU), a convolutional-transformer deep learning model, and five strategies for multitemporal global-scale glacier mapping using open satellite imagery. Assessing the spatial, temporal and cross-sensor generalisation shows that our best strategy achieves intersection over union >0.85 on previously unobserved images in most cases, which drops to >0.75 for debris-rich areas such as High-Mountain Asia and increases to >0.90 for regions dominated by clean ice. Additionally, adding synthetic aperture radar data, namely, backscatter and interferometric coherence, increases the accuracy in all regions where available. The calibrated confidence for glacier extents is reported making the predictions more reliable and interpretable. We also release a benchmark dataset that covers 9% of glaciers worldwide. Our results support efforts towards automated multitemporal and global glacier mapping.

• Maslov, Konstantin; Persello, Claudio; Schellenberger, Thomas & Stein, Alfred (2023). GLAVITU: A Hybrid CNN-Transformer for Multi-Regional Glacier Mapping from Multi-Source Data, IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium. IEEE conference proceedings. ISSN 9798350320107. p. 1233–1236. doi: 10.1109/IGARSS52108.2023.10281828. Full text in Research Archive Show summary

  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.

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• Maslov, Konstantin; Persello, Claudio; Schellenberger, Thomas & Stein, Alfred (2023). GLAVITU: A Hybrid CNN-Transformer for Multi-Regional Glacier Mapping from Multi-Source Data. Show summary

  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.

• Hänsch, Ronny; Rußwurm, Marc; Roscher, Ribana & Persello, Claudio (2023). IGARSS 2023 Tutorial: Machine Learning in Remote Sensing - Theory and Applications for Earth Observation.
• Maslov, Konstantin; Persello, Claudio; Schellenberger, Thomas & Stein, Alfred (2023). Towards the First Global Multitemporal Data Set of Glacier Outlines.
• Racoviteanu, Adina E.; Miles, Evan; Davies, Bethan; Bolch, Tobias; Watson, C. Scott & Buri, Pascal [Show all 14 contributors for this article] (2023). Intercomparison of remotely sensed methods for delineation of high mountain debris-covered glaciers using machine learning. Show summary

  Glaciers are important contributors to water resources in many parts of the world, as well as constituting potential hazards. Monitoring of glaciers with remote sensing data is complicated by the presence of supraglacial debris which limits the effectiveness of semi-automated glacier delineation techniques. Furthermore, debris-covered glaciers are notoriously hard to survey in the field due to their complex, chaotic topography, and the presence of ephemeral ice cliffs and supraglacial lakes. As such, debris-covered glaciers are poorly represented in global inventories such as GLIMS and RGI, which consist of an assemblage of outlines from various dates. These inventories are subject to uncertainties due to mapping by multi-analysts using different methods. Despite recent efforts to map supraglacial debris cover at regional level, there remains an urgent need to develop a global, robust automated mapping approach based on open access data. Novel remote sensing data including high-resolution optical and radar data combined with emerging machine learning offer unique opportunities to advance current mapping methods. Here we evaluate current indices used in mapping supraglacial debris-cover and derive a best-practice workflow recommendation from particular combinations of these indices to derive glacier outlines for six representative subregions chosen globally: Khumbu and Manaslu regions (Nepal), Cordillera Blanca (Peru), Northern Patagonian Ice fields, Alaska Wrangell range, Hunza valley (Karakoram) and the Tien Shan. We present preliminary results of machine learning algorithms that combine the various remote sensing indices. The aim is to develop a robust, systematic method to map debris cover in a consistent manner at multi-temporal scales.

• Maslov, Konstantin; Schellenberger, Thomas; Persello, Claudio & Stein, Alfred (2022). Towards Fully-Automated Glacier Mapping in Arctic Regions using Deep Learning.
• Maslov, Konstantin & Schellenberger, Thomas (2022). Deep Learning for Glacier Mapping.
• Pandit, Prashant H; Persello, Claudio; Aghababaei, Hossein; Schellenberger, Thomas & Maslov, Konstantin (2022). How precisely can we map the glaciers?
• Schellenberger, Thomas & Maslov, Konstantin (2022). Remote Sensing in Glaciology – the traditional basics.

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