Machine Learning for Sea Ice Monitoring from Satellites
Today, radar imaging from space allows continuous and wide-area sea ice monitoring under nearly all weather conditions. To this end, we applied modern machine learning techniques to produce ice-describing semantic maps of the polar regions of the Earth. Time series of these maps can then be exploite...
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ftdlr:oai:elib.dlr.de:130273 2024-05-19T07:48:18+00:00 Machine Learning for Sea Ice Monitoring from Satellites Dumitru, Corneliu Octavian Andrei, Vlad Schwarz, Gottfried Datcu, Mihai 2019 application/pdf https://elib.dlr.de/130273/ https://elib.dlr.de/130273/1/Machine%20Learning%20for%20Sea%20Ice%20Monitoring%20from%20Satellites_poster.pdf http://www.pf.bgu.tum.de/isprs/mrss19/ en eng https://elib.dlr.de/130273/1/Machine%20Learning%20for%20Sea%20Ice%20Monitoring%20from%20Satellites_poster.pdf Dumitru, Corneliu Octavian und Andrei, Vlad und Schwarz, Gottfried und Datcu, Mihai (2019) Machine Learning for Sea Ice Monitoring from Satellites. In: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives. Munich Remote Sensing Symposium 2019, 2019-09-18 - 2019-09-20, Munich, Germany. doi:10.5194/isprs-archives-XLII-2-W16-83-2019 <https://doi.org/10.5194/isprs-archives-XLII-2-W16-83-2019>. ISSN 1682-1750. EO Data Science Konferenzbeitrag NonPeerReviewed 2019 ftdlr https://doi.org/10.5194/isprs-archives-XLII-2-W16-83-2019 2024-04-25T00:51:33Z Today, radar imaging from space allows continuous and wide-area sea ice monitoring under nearly all weather conditions. To this end, we applied modern machine learning techniques to produce ice-describing semantic maps of the polar regions of the Earth. Time series of these maps can then be exploited for local and regional change maps of selected areas. What we expect, however, are fully-automated unsupervised routine classifications of sea ice regions that are needed for the rapid and reliable monitoring of shipping routes, drifting and disintegrating icebergs, snowfall and melting on ice, and other dynamic climate change indicators. Therefore, we designed and implemented an automated processing chain that analyses and interprets the specific ice-related content of high-resolution synthetic aperture radar (SAR) images. We trained this system with selected images covering various use cases allowing us to interpret these images with modern machine learning approaches. In the following, we describe a system comprising representation learning, variational inference, and auto-encoders. Test runs have already demonstrated its usefulness and stability that can pave the way towards future artificial intelligence systems extending, for instance, the current capabilities of traditional image analysis by including content-related image understanding. Conference Object Sea ice German Aerospace Center: elib - DLR electronic library The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W16 83 89 |
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German Aerospace Center: elib - DLR electronic library |
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English |
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EO Data Science |
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EO Data Science Dumitru, Corneliu Octavian Andrei, Vlad Schwarz, Gottfried Datcu, Mihai Machine Learning for Sea Ice Monitoring from Satellites |
topic_facet |
EO Data Science |
description |
Today, radar imaging from space allows continuous and wide-area sea ice monitoring under nearly all weather conditions. To this end, we applied modern machine learning techniques to produce ice-describing semantic maps of the polar regions of the Earth. Time series of these maps can then be exploited for local and regional change maps of selected areas. What we expect, however, are fully-automated unsupervised routine classifications of sea ice regions that are needed for the rapid and reliable monitoring of shipping routes, drifting and disintegrating icebergs, snowfall and melting on ice, and other dynamic climate change indicators. Therefore, we designed and implemented an automated processing chain that analyses and interprets the specific ice-related content of high-resolution synthetic aperture radar (SAR) images. We trained this system with selected images covering various use cases allowing us to interpret these images with modern machine learning approaches. In the following, we describe a system comprising representation learning, variational inference, and auto-encoders. Test runs have already demonstrated its usefulness and stability that can pave the way towards future artificial intelligence systems extending, for instance, the current capabilities of traditional image analysis by including content-related image understanding. |
format |
Conference Object |
author |
Dumitru, Corneliu Octavian Andrei, Vlad Schwarz, Gottfried Datcu, Mihai |
author_facet |
Dumitru, Corneliu Octavian Andrei, Vlad Schwarz, Gottfried Datcu, Mihai |
author_sort |
Dumitru, Corneliu Octavian |
title |
Machine Learning for Sea Ice Monitoring from Satellites |
title_short |
Machine Learning for Sea Ice Monitoring from Satellites |
title_full |
Machine Learning for Sea Ice Monitoring from Satellites |
title_fullStr |
Machine Learning for Sea Ice Monitoring from Satellites |
title_full_unstemmed |
Machine Learning for Sea Ice Monitoring from Satellites |
title_sort |
machine learning for sea ice monitoring from satellites |
publishDate |
2019 |
url |
https://elib.dlr.de/130273/ https://elib.dlr.de/130273/1/Machine%20Learning%20for%20Sea%20Ice%20Monitoring%20from%20Satellites_poster.pdf http://www.pf.bgu.tum.de/isprs/mrss19/ |
genre |
Sea ice |
genre_facet |
Sea ice |
op_relation |
https://elib.dlr.de/130273/1/Machine%20Learning%20for%20Sea%20Ice%20Monitoring%20from%20Satellites_poster.pdf Dumitru, Corneliu Octavian und Andrei, Vlad und Schwarz, Gottfried und Datcu, Mihai (2019) Machine Learning for Sea Ice Monitoring from Satellites. In: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives. Munich Remote Sensing Symposium 2019, 2019-09-18 - 2019-09-20, Munich, Germany. doi:10.5194/isprs-archives-XLII-2-W16-83-2019 <https://doi.org/10.5194/isprs-archives-XLII-2-W16-83-2019>. ISSN 1682-1750. |
op_doi |
https://doi.org/10.5194/isprs-archives-XLII-2-W16-83-2019 |
container_title |
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
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XLII-2/W16 |
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83 |
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89 |
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1799488854439231488 |