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

Konstantin A. Maslov*, Claudio Persello, Thomas Schellenberger, Alfred Stein

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

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.

Original languageEnglish
Title of host publicationIGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, Proceedings
PublisherIEEE
Pages1233-1236
Number of pages4
ISBN (Electronic)9798350320107
DOIs
Publication statusPublished - 2023
Event43rd IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2023 - Pasadena Convention Center, Pasadena, United States
Duration: 16 Jul 202321 Jul 2023
Conference number: 43
https://2023.ieeeigarss.org/index.php

Publication series

NameInternational Geoscience and Remote Sensing Symposium (IGARSS)
Volume2023-July

Conference

Conference43rd IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2023
Abbreviated titleIGARSS 2023
Country/TerritoryUnited States
CityPasadena
Period16/07/2321/07/23
Internet address

Keywords

  • convolutional neural network
  • deep learning
  • Glacier mapping
  • vision transformer
  • 2024 OA procedure

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