Evaluating a deep-learning approach for mapping retrogressive thaw slumps across the Arctic
Retrogressive thaw slumps (RTS) are typical landforms indicating processes of rapid thawing and degrading permafrost. Their abundance is increasing in many regions and quantifying their dynamics is of high importance for assessing geomorphic, hydrologic, and biogeochemical impacts of climate change...
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2021
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ftawi:oai:epic.awi.de:55337 2023-05-15T14:26:55+02:00 Evaluating a deep-learning approach for mapping retrogressive thaw slumps across the Arctic Nitze, Ingmar Heidler, Konrad Barth, Sophia Guido, Grosse Targowicka, Alexandra 2021-12-17 https://epic.awi.de/id/eprint/55337/ https://hdl.handle.net/10013/epic.bf42dc2a-edd8-480e-829c-ffbfb003f1ce unknown American Geophysical Union Nitze, I. orcid:0000-0002-1165-6852 , Heidler, K. , Barth, S. , Guido, G. orcid:0000-0001-5895-2141 and Targowicka, A. (2021) Evaluating a deep-learning approach for mapping retrogressive thaw slumps across the Arctic , AGU Fall Meeting 2021, Online, 13 December 2021 - 17 December 2021 . hdl:10013/epic.bf42dc2a-edd8-480e-829c-ffbfb003f1ce EPIC3AGU Fall Meeting 2021, Online, 2021-12-13-2021-12-17American Geophysical Union Conference notRev 2021 ftawi 2021-12-27T00:08:59Z Retrogressive thaw slumps (RTS) are typical landforms indicating processes of rapid thawing and degrading permafrost. Their abundance is increasing in many regions and quantifying their dynamics is of high importance for assessing geomorphic, hydrologic, and biogeochemical impacts of climate change in the Arctic. Here we present a deep-learning (DL) based semantic segmentation framework to detect RTS, using high-resolution multi-spectral PlanetScope, topographic (ArcticDEM elevation and slope), and medium-resolution multi-temporal Landsat Trend data. We created a highly automated processing pipeline, which is designed to allow reproducible results and to be flexible for multiple input data types. The processing workflow is based on the pytorch deep-learning framework and includes a variety of different segmentation architectures (UNet, UNet++, DeepLabV3), backbones and includes common data transformation techniques such as augmentation or normalization. We tested (training, validation) our DL based model in six different regions of 100 to 300 km² size across Canada, and Siberia. We performed a regional cross-validation (5 regions training, 1 region validation) to test the spatial robustness and transferability of the algorithm. Furthermore, we tested different architectures, backbones and loss-functions to identify the best performing and most robust parameter sets. For training the models we created a database of manually digitized and validated RTS polygons. The resulting model performance varied strongly between different regions with maximum Intersection over Union (IoU) scores between 0.15 and 0.58. The strong regional variation emphasizes the need for sufficiently large training data, which is representative of the diversity of RTS types. However, the creation of good training data proved to be challenging due to the fuzzy definition and delineation of RTS. We are further continuing to improve the usability and the functionality to add further datasets and classes. We will show first results from the upscaling beyond small test areas towards large spatial clusters of extensive RTS presence e.g. Peel Plateau in NW Canada. Conference Object Arctic Arctic Climate change permafrost Siberia Alfred Wegener Institute for Polar- and Marine Research (AWI): ePIC (electronic Publication Information Center) Arctic Canada |
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Alfred Wegener Institute for Polar- and Marine Research (AWI): ePIC (electronic Publication Information Center) |
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description |
Retrogressive thaw slumps (RTS) are typical landforms indicating processes of rapid thawing and degrading permafrost. Their abundance is increasing in many regions and quantifying their dynamics is of high importance for assessing geomorphic, hydrologic, and biogeochemical impacts of climate change in the Arctic. Here we present a deep-learning (DL) based semantic segmentation framework to detect RTS, using high-resolution multi-spectral PlanetScope, topographic (ArcticDEM elevation and slope), and medium-resolution multi-temporal Landsat Trend data. We created a highly automated processing pipeline, which is designed to allow reproducible results and to be flexible for multiple input data types. The processing workflow is based on the pytorch deep-learning framework and includes a variety of different segmentation architectures (UNet, UNet++, DeepLabV3), backbones and includes common data transformation techniques such as augmentation or normalization. We tested (training, validation) our DL based model in six different regions of 100 to 300 km² size across Canada, and Siberia. We performed a regional cross-validation (5 regions training, 1 region validation) to test the spatial robustness and transferability of the algorithm. Furthermore, we tested different architectures, backbones and loss-functions to identify the best performing and most robust parameter sets. For training the models we created a database of manually digitized and validated RTS polygons. The resulting model performance varied strongly between different regions with maximum Intersection over Union (IoU) scores between 0.15 and 0.58. The strong regional variation emphasizes the need for sufficiently large training data, which is representative of the diversity of RTS types. However, the creation of good training data proved to be challenging due to the fuzzy definition and delineation of RTS. We are further continuing to improve the usability and the functionality to add further datasets and classes. We will show first results from the upscaling beyond small test areas towards large spatial clusters of extensive RTS presence e.g. Peel Plateau in NW Canada. |
format |
Conference Object |
author |
Nitze, Ingmar Heidler, Konrad Barth, Sophia Guido, Grosse Targowicka, Alexandra |
spellingShingle |
Nitze, Ingmar Heidler, Konrad Barth, Sophia Guido, Grosse Targowicka, Alexandra Evaluating a deep-learning approach for mapping retrogressive thaw slumps across the Arctic |
author_facet |
Nitze, Ingmar Heidler, Konrad Barth, Sophia Guido, Grosse Targowicka, Alexandra |
author_sort |
Nitze, Ingmar |
title |
Evaluating a deep-learning approach for mapping retrogressive thaw slumps across the Arctic |
title_short |
Evaluating a deep-learning approach for mapping retrogressive thaw slumps across the Arctic |
title_full |
Evaluating a deep-learning approach for mapping retrogressive thaw slumps across the Arctic |
title_fullStr |
Evaluating a deep-learning approach for mapping retrogressive thaw slumps across the Arctic |
title_full_unstemmed |
Evaluating a deep-learning approach for mapping retrogressive thaw slumps across the Arctic |
title_sort |
evaluating a deep-learning approach for mapping retrogressive thaw slumps across the arctic |
publisher |
American Geophysical Union |
publishDate |
2021 |
url |
https://epic.awi.de/id/eprint/55337/ https://hdl.handle.net/10013/epic.bf42dc2a-edd8-480e-829c-ffbfb003f1ce |
geographic |
Arctic Canada |
geographic_facet |
Arctic Canada |
genre |
Arctic Arctic Climate change permafrost Siberia |
genre_facet |
Arctic Arctic Climate change permafrost Siberia |
op_source |
EPIC3AGU Fall Meeting 2021, Online, 2021-12-13-2021-12-17American Geophysical Union |
op_relation |
Nitze, I. orcid:0000-0002-1165-6852 , Heidler, K. , Barth, S. , Guido, G. orcid:0000-0001-5895-2141 and Targowicka, A. (2021) Evaluating a deep-learning approach for mapping retrogressive thaw slumps across the Arctic , AGU Fall Meeting 2021, Online, 13 December 2021 - 17 December 2021 . hdl:10013/epic.bf42dc2a-edd8-480e-829c-ffbfb003f1ce |
_version_ |
1766300411816312832 |