Using deep learning in operational data products – Lessons learned from the IceLines dataset on Antarctic ice shelf front change ...
<!--!introduction!--> Antarctica’s ice shelves are the floating extensions of the ice sheet. The discharge of the Antarctic ice sheet increases if ice shelf areas with strong buttressing forces are lost. This has direct implications on Antarctica’s contribution to global sea level rise. Theref...
| Main Authors: | , , , |
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| Format: | Conference Object |
| Language: | unknown |
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GFZ German Research Centre for Geosciences
2023
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| Online Access: | https://dx.doi.org/10.57757/iugg23-2738 https://gfzpublic.gfz-potsdam.de/pubman/item/item_5019135 |
| Summary: | <!--!introduction!--> Antarctica’s ice shelves are the floating extensions of the ice sheet. The discharge of the Antarctic ice sheet increases if ice shelf areas with strong buttressing forces are lost. This has direct implications on Antarctica’s contribution to global sea level rise. Therefore, it is important to have an operational product constantly providing data on ice shelf front position to locate and track changes in ice shelf area. Here, we present the workflow of the IceLines dataset showcasing a processing pipeline from acquired satellite data to a deep learning (DL) derived data product. The workflow includes the following steps: (1) triggering data download (2) pre-processing of Sentinel-1 SAR data with Docker on a high-performance cluster (3) training a convolutional neural network (CNN) for different input data formats (4) inference for ice shelf front detection (5) post-processing of the CNN output (6) sanity check of front positions based on the existing time series (7) automated ... : The 28th IUGG General Assembly (IUGG2023) (Berlin 2023) ... |
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