Combining “Deep Learning” and Physically Constrained Neural Networks to Derive Complex Glaciological Change Processes from Modern High-Resolution Satellite Imagery: Application of the GEOCLASS-image System to Create VarioCNN for Glacier Surges
The objectives of this paper are to investigate the tradeoffs between a physically constrained neural network and a deep, convolutional neural network and to design a combined ML approach (“VarioCNN”). Our solution is provided in the framework of a cyberinfrastructure that includes a newly designed...
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2024
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Online Access: | http://dx.doi.org/10.22541/essoar.170585931.19680198/v1 |
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crwinnower:10.22541/essoar.170585931.19680198/v1 2024-06-02T08:07:07+00:00 Combining “Deep Learning” and Physically Constrained Neural Networks to Derive Complex Glaciological Change Processes from Modern High-Resolution Satellite Imagery: Application of the GEOCLASS-image System to Create VarioCNN for Glacier Surges Herzfeld, Ute Christina Hessburg, Lawrence John Trantow, Thomas Hayes, Adam N 2024 http://dx.doi.org/10.22541/essoar.170585931.19680198/v1 unknown Authorea, Inc. posted-content 2024 crwinnower https://doi.org/10.22541/essoar.170585931.19680198/v1 2024-05-07T14:19:22Z The objectives of this paper are to investigate the tradeoffs between a physically constrained neural network and a deep, convolutional neural network and to design a combined ML approach (“VarioCNN”). Our solution is provided in the framework of a cyberinfrastructure that includes a newly designed ML software, GEOCLASS-image, modern high-resolution satellite image datasets (Maxar WorldView data) and instructions/descriptions that may facilitate solving similar spatial classification problems. Combining the advantages of the physically-driven connectionist-geostatistical classification method with those of an efficient CNN, VarioCNN, provides a means for rapid and efficient extraction of complex geophysical information from submeter resolution satellite imagery. A retraining loop overcomes the difficulties of creating a labeled training data set. Computational analyses and developments are centered on a specific, but generalizable, geophysical problem: The classification of crevasse types that form during the surge of a glacier system. A surge is a glacial catastrophe, an acceleration of a glacier to typically 100-200 times its normal velocity, which for a marine-terminating glacier leads to sudden and substantial mass transfer from the cryosphere to the oceans, contributing significantly to sea-level-rise. The sudden and rapid acceleration characteristic of a surge results in formation of crevasses, whose spatial characteristics provide informants on the ice-dynamic processes that occur during the surge. GEOCLASS-image is applied to study the current (2016-2024) surge in the Negribreen Glacier System, Svalbard. The geophysical result is a description of the structural evolution and expansion of the surge, based on crevasse types that capture ice deformation in 6 simplified classes. Other/Unknown Material glacier Svalbard The Winnower Negribreen ENVELOPE(19.150,19.150,78.564,78.564) Svalbard |
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The objectives of this paper are to investigate the tradeoffs between a physically constrained neural network and a deep, convolutional neural network and to design a combined ML approach (“VarioCNN”). Our solution is provided in the framework of a cyberinfrastructure that includes a newly designed ML software, GEOCLASS-image, modern high-resolution satellite image datasets (Maxar WorldView data) and instructions/descriptions that may facilitate solving similar spatial classification problems. Combining the advantages of the physically-driven connectionist-geostatistical classification method with those of an efficient CNN, VarioCNN, provides a means for rapid and efficient extraction of complex geophysical information from submeter resolution satellite imagery. A retraining loop overcomes the difficulties of creating a labeled training data set. Computational analyses and developments are centered on a specific, but generalizable, geophysical problem: The classification of crevasse types that form during the surge of a glacier system. A surge is a glacial catastrophe, an acceleration of a glacier to typically 100-200 times its normal velocity, which for a marine-terminating glacier leads to sudden and substantial mass transfer from the cryosphere to the oceans, contributing significantly to sea-level-rise. The sudden and rapid acceleration characteristic of a surge results in formation of crevasses, whose spatial characteristics provide informants on the ice-dynamic processes that occur during the surge. GEOCLASS-image is applied to study the current (2016-2024) surge in the Negribreen Glacier System, Svalbard. The geophysical result is a description of the structural evolution and expansion of the surge, based on crevasse types that capture ice deformation in 6 simplified classes. |
format |
Other/Unknown Material |
author |
Herzfeld, Ute Christina Hessburg, Lawrence John Trantow, Thomas Hayes, Adam N |
spellingShingle |
Herzfeld, Ute Christina Hessburg, Lawrence John Trantow, Thomas Hayes, Adam N Combining “Deep Learning” and Physically Constrained Neural Networks to Derive Complex Glaciological Change Processes from Modern High-Resolution Satellite Imagery: Application of the GEOCLASS-image System to Create VarioCNN for Glacier Surges |
author_facet |
Herzfeld, Ute Christina Hessburg, Lawrence John Trantow, Thomas Hayes, Adam N |
author_sort |
Herzfeld, Ute Christina |
title |
Combining “Deep Learning” and Physically Constrained Neural Networks to Derive Complex Glaciological Change Processes from Modern High-Resolution Satellite Imagery: Application of the GEOCLASS-image System to Create VarioCNN for Glacier Surges |
title_short |
Combining “Deep Learning” and Physically Constrained Neural Networks to Derive Complex Glaciological Change Processes from Modern High-Resolution Satellite Imagery: Application of the GEOCLASS-image System to Create VarioCNN for Glacier Surges |
title_full |
Combining “Deep Learning” and Physically Constrained Neural Networks to Derive Complex Glaciological Change Processes from Modern High-Resolution Satellite Imagery: Application of the GEOCLASS-image System to Create VarioCNN for Glacier Surges |
title_fullStr |
Combining “Deep Learning” and Physically Constrained Neural Networks to Derive Complex Glaciological Change Processes from Modern High-Resolution Satellite Imagery: Application of the GEOCLASS-image System to Create VarioCNN for Glacier Surges |
title_full_unstemmed |
Combining “Deep Learning” and Physically Constrained Neural Networks to Derive Complex Glaciological Change Processes from Modern High-Resolution Satellite Imagery: Application of the GEOCLASS-image System to Create VarioCNN for Glacier Surges |
title_sort |
combining “deep learning” and physically constrained neural networks to derive complex glaciological change processes from modern high-resolution satellite imagery: application of the geoclass-image system to create variocnn for glacier surges |
publisher |
Authorea, Inc. |
publishDate |
2024 |
url |
http://dx.doi.org/10.22541/essoar.170585931.19680198/v1 |
long_lat |
ENVELOPE(19.150,19.150,78.564,78.564) |
geographic |
Negribreen Svalbard |
geographic_facet |
Negribreen Svalbard |
genre |
glacier Svalbard |
genre_facet |
glacier Svalbard |
op_doi |
https://doi.org/10.22541/essoar.170585931.19680198/v1 |
_version_ |
1800752131240624128 |