Glacier mapping from Sentinel-1 SAR time series with deep learning in Svalbard

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

*Corresponding author for this work

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

Abstract

Glaciers are one of the essential climate variables. Tracking their areal changes over time is of high importance for monitoring the impacts of climate change and designing adaptation strategies. Mapping glaciers from optical remote sensing data might result in a very limited temporal resolution due to the absence of cloud-free imagery at the end of the ablation season. Synthetic aperture radar (SAR) solves this problem as it can operate in almost all weather conditions. Here, we present a deep learning strategy for glacier mapping based solely on Sentinel-1 SAR data in Svalbard. We test two options for integrating SAR image time series into deep learning models, namely, 3D convolutions and long short-term memory (LSTM) cells. Both proposed models achieve an intersection over union (IoU) of 0.964 on the test subset. Our results highlight the applicability of SAR data in glacier mapping with the potential to obtain glacier inventories with higher temporal resolution. We shared our dataset, code-base and pretrained models at https://github.com/konstantin-a-maslov/icemapper.

Original languageEnglish
Title of host publicationIGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium, Proceedings
PublisherIEEE
Pages14-17
Number of pages4
ISBN (Electronic)9798350360325
DOIs
Publication statusPublished - 5 Sept 2024
Event2024 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2024 - Athens, Greece
Duration: 7 Jul 202412 Jul 2024

Publication series

NameInternational Geoscience and Remote Sensing Symposium (IGARSS)

Conference

Conference2024 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2024
Abbreviated titleIGARSS
Country/TerritoryGreece
CityAthens
Period7/07/2412/07/24

Keywords

  • 3D convolution
  • deep learning
  • Glacier mapping
  • long short-term memory
  • Svalbard
  • synthetic aperture radar
  • 2024 OA procedure

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