Semantic Segmentation with Deep Convolutional Neural Networks for Automated Dust Detection in Goes-R Satellite Imagery Article Swipe
Talha Ali Khan
,
Nicholas Elmer
,
Muthukumaran Ramasubramanian
,
Emily Berndt
,
Iksha Gurung
,
Aaron Kaulfus
,
Manil Maskey
,
Rahul Ramachandran
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1002/essoar.10506334.1
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1002/essoar.10506334.1
Earth and Space Science Open Archive Presented WorkOpen AccessYou are viewing the latest version by default [v1]Semantic Segmentation with Deep Convolutional Neural Networks for Automated Dust Detection in Goes-R Satellite ImageryAuthorsTalhaKhaniDNicholasElmeriDMuthukumaranRamasubramanianEmilyBerndtIkshaGurungAaronKaulfusManilMaskeyiDRahulRamachandranSee all authors Talha KhaniDCorresponding Author• Submitting AuthorNASA Marshall Space Flight CenteriDhttps://orcid.org/0000-0003-3874-4075view email addressThe email was not providedcopy email addressNicholas ElmeriDUniversity of Alabama in HuntsvilleiDhttps://orcid.org/0000-0002-6343-6210view email addressThe email was not providedcopy email addressMuthukumaran RamasubramanianUniversity of Alabama in Huntsvilleview email addressThe email was not providedcopy email addressEmily BerndtNASA Marshall Space Flight Centerview email addressThe email was not providedcopy email addressIksha GurungUniversity of Alabama in Huntsvilleview email addressThe email was not providedcopy email addressAaron KaulfusUniversity of Alabama in Huntsvilleview email addressThe email was not providedcopy email addressManil MaskeyiDUniversity of Alabama in HuntsvilleiDhttps://orcid.org/0000-0002-5087-6903view email addressThe email was not providedcopy email addressRahul RamachandranNASA Marshall Space Flight Centerview email addressThe email was not providedcopy email address
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1002/essoar.10506334.1
- OA Status
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https://openalex.org/W3137978387Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1002/essoar.10506334.1Digital Object Identifier
- Title
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Semantic Segmentation with Deep Convolutional Neural Networks for Automated Dust Detection in Goes-R Satellite ImageryWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2021Year of publication
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2021-02-26Full publication date if available
- Authors
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Talha Ali Khan, Nicholas Elmer, Muthukumaran Ramasubramanian, Emily Berndt, Iksha Gurung, Aaron Kaulfus, Manil Maskey, Rahul RamachandranList of authors in order
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https://doi.org/10.1002/essoar.10506334.1Publisher landing page
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://doi.org/10.1002/essoar.10506334.1Direct OA link when available
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Convolutional neural network, Computer science, World Wide Web, Satellite, Artificial intelligence, Physics, AstronomyTop concepts (fields/topics) attached by OpenAlex
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1Total citation count in OpenAlex
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2022: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.Huntsvilleview | 67, 93, 106 |
| abstract_inverted_index.addressNicholas | 49 |
| abstract_inverted_index.GurungUniversity | 89 |
| abstract_inverted_index.RamachandranNASA | 128 |
| abstract_inverted_index.ElmeriDUniversity | 50 |
| abstract_inverted_index.KaulfusUniversity | 102 |
| abstract_inverted_index.MaskeyiDUniversity | 115 |
| abstract_inverted_index.KhaniDCorresponding | 34 |
| abstract_inverted_index.addressMuthukumaran | 62 |
| abstract_inverted_index.RamasubramanianUniversity | 63 |
| abstract_inverted_index.CenteriDhttps://orcid.org/0000-0003-3874-4075view | 41 |
| abstract_inverted_index.HuntsvilleiDhttps://orcid.org/0000-0002-5087-6903view | 119 |
| abstract_inverted_index.HuntsvilleiDhttps://orcid.org/0000-0002-6343-6210view | 54 |
| abstract_inverted_index.ImageryAuthorsTalhaKhaniDNicholasElmeriDMuthukumaranRamasubramanianEmilyBerndtIkshaGurungAaronKaulfusManilMaskeyiDRahulRamachandranSee | 30 |
| cited_by_percentile_year.max | 94 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 8 |
| citation_normalized_percentile.value | 0.36419253 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |