Real-time GeoAI for High-resolution Mapping and Segmentation of Arctic Permafrost Features ...
This paper introduces a real-time GeoAI workflow for large-scale image analysis and the segmentation of Arctic permafrost features at a fine-granularity. Very high-resolution (0.5m) commercial imagery is used in this analysis. To achieve real-time prediction, our workflow employs a lightweight, deep...
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2023
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Online Access: | https://dx.doi.org/10.48550/arxiv.2306.05341 https://arxiv.org/abs/2306.05341 |
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ftdatacite:10.48550/arxiv.2306.05341 2023-07-23T04:17:19+02:00 Real-time GeoAI for High-resolution Mapping and Segmentation of Arctic Permafrost Features ... Li, Wenwen Hsu, Chia-Yu Wang, Sizhe Witharana, Chandi Liljedahl, Anna 2023 https://dx.doi.org/10.48550/arxiv.2306.05341 https://arxiv.org/abs/2306.05341 unknown arXiv Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 Computer Vision and Pattern Recognition cs.CV FOS Computer and information sciences CreativeWork Preprint article Article 2023 ftdatacite https://doi.org/10.48550/arxiv.2306.05341 2023-07-03T18:36:31Z This paper introduces a real-time GeoAI workflow for large-scale image analysis and the segmentation of Arctic permafrost features at a fine-granularity. Very high-resolution (0.5m) commercial imagery is used in this analysis. To achieve real-time prediction, our workflow employs a lightweight, deep learning-based instance segmentation model, SparseInst, which introduces and uses Instance Activation Maps to accurately locate the position of objects within the image scene. Experimental results show that the model can achieve better accuracy of prediction at a much faster inference speed than the popular Mask-RCNN model. ... Report Arctic permafrost DataCite Metadata Store (German National Library of Science and Technology) Arctic |
institution |
Open Polar |
collection |
DataCite Metadata Store (German National Library of Science and Technology) |
op_collection_id |
ftdatacite |
language |
unknown |
topic |
Computer Vision and Pattern Recognition cs.CV FOS Computer and information sciences |
spellingShingle |
Computer Vision and Pattern Recognition cs.CV FOS Computer and information sciences Li, Wenwen Hsu, Chia-Yu Wang, Sizhe Witharana, Chandi Liljedahl, Anna Real-time GeoAI for High-resolution Mapping and Segmentation of Arctic Permafrost Features ... |
topic_facet |
Computer Vision and Pattern Recognition cs.CV FOS Computer and information sciences |
description |
This paper introduces a real-time GeoAI workflow for large-scale image analysis and the segmentation of Arctic permafrost features at a fine-granularity. Very high-resolution (0.5m) commercial imagery is used in this analysis. To achieve real-time prediction, our workflow employs a lightweight, deep learning-based instance segmentation model, SparseInst, which introduces and uses Instance Activation Maps to accurately locate the position of objects within the image scene. Experimental results show that the model can achieve better accuracy of prediction at a much faster inference speed than the popular Mask-RCNN model. ... |
format |
Report |
author |
Li, Wenwen Hsu, Chia-Yu Wang, Sizhe Witharana, Chandi Liljedahl, Anna |
author_facet |
Li, Wenwen Hsu, Chia-Yu Wang, Sizhe Witharana, Chandi Liljedahl, Anna |
author_sort |
Li, Wenwen |
title |
Real-time GeoAI for High-resolution Mapping and Segmentation of Arctic Permafrost Features ... |
title_short |
Real-time GeoAI for High-resolution Mapping and Segmentation of Arctic Permafrost Features ... |
title_full |
Real-time GeoAI for High-resolution Mapping and Segmentation of Arctic Permafrost Features ... |
title_fullStr |
Real-time GeoAI for High-resolution Mapping and Segmentation of Arctic Permafrost Features ... |
title_full_unstemmed |
Real-time GeoAI for High-resolution Mapping and Segmentation of Arctic Permafrost Features ... |
title_sort |
real-time geoai for high-resolution mapping and segmentation of arctic permafrost features ... |
publisher |
arXiv |
publishDate |
2023 |
url |
https://dx.doi.org/10.48550/arxiv.2306.05341 https://arxiv.org/abs/2306.05341 |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic permafrost |
genre_facet |
Arctic permafrost |
op_rights |
Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 |
op_doi |
https://doi.org/10.48550/arxiv.2306.05341 |
_version_ |
1772178906496892928 |