Searching similar weather maps using convolutional autoencoder and satellite images
A weather forecaster predicts the weather by analyzing current weather map images generated by a satellite. In this analyzing process, the accuracy of the prediction depends highly on the forecaster’s experience which is needed to recollect similar weather maps from the past. In an attempt to help f...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2023-02-01
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| Series: | ICT Express |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405959522000546 |
| _version_ | 1797900658946342912 |
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| author | Heewoong Ahn Sunhwa Lee Hanseok Ko Meejoung Kim Sung Won Han Junhee Seok |
| author_facet | Heewoong Ahn Sunhwa Lee Hanseok Ko Meejoung Kim Sung Won Han Junhee Seok |
| author_sort | Heewoong Ahn |
| collection | DOAJ |
| description | A weather forecaster predicts the weather by analyzing current weather map images generated by a satellite. In this analyzing process, the accuracy of the prediction depends highly on the forecaster’s experience which is needed to recollect similar weather maps from the past. In an attempt to help forecasters to obtain empirical data and analyze the current weather status, this paper proposes a convolutional autoencoder model to find weather maps from the past that are similar to a current weather map by extracting the latent features of each image. To measure the similarity between each pair of images, metrics including mean squared error and structural similarity were used and case studies for searching similar satellite images were conducted and visualized. The paper also demonstrates that searching similar weather maps can be useful guidance to all forecasters when analyzing and predicting the weather. |
| first_indexed | 2024-04-10T08:49:28Z |
| format | Article |
| id | doaj.art-c7a6b06649934bc08d7c6625fbe02212 |
| institution | Directory Open Access Journal |
| issn | 2405-9595 |
| language | English |
| last_indexed | 2024-04-10T08:49:28Z |
| publishDate | 2023-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | ICT Express |
| spelling | doaj.art-c7a6b06649934bc08d7c6625fbe022122023-02-22T04:32:01ZengElsevierICT Express2405-95952023-02-01916975Searching similar weather maps using convolutional autoencoder and satellite imagesHeewoong Ahn0Sunhwa Lee1Hanseok Ko2Meejoung Kim3Sung Won Han4Junhee Seok5Department of Electrical Engineering, Korea University, Anam-dong, Seoul, 136-713, South KoreaDepartment of Industrial Management Engineering, Korea University, Anam-dong, Seoul, 136-713, South KoreaDepartment of Electrical Engineering, Korea University, Anam-dong, Seoul, 136-713, South KoreaResearch Institute for Information and Communication Technology, Korea University, Anam-dong, Seoul, 136-713, South KoreaDepartment of Industrial Management Engineering, Korea University, Anam-dong, Seoul, 136-713, South Korea; Co-corresponding author.Department of Electrical Engineering, Korea University, Anam-dong, Seoul, 136-713, South Korea; Co-corresponding author.A weather forecaster predicts the weather by analyzing current weather map images generated by a satellite. In this analyzing process, the accuracy of the prediction depends highly on the forecaster’s experience which is needed to recollect similar weather maps from the past. In an attempt to help forecasters to obtain empirical data and analyze the current weather status, this paper proposes a convolutional autoencoder model to find weather maps from the past that are similar to a current weather map by extracting the latent features of each image. To measure the similarity between each pair of images, metrics including mean squared error and structural similarity were used and case studies for searching similar satellite images were conducted and visualized. The paper also demonstrates that searching similar weather maps can be useful guidance to all forecasters when analyzing and predicting the weather.http://www.sciencedirect.com/science/article/pii/S2405959522000546Deep learningWeather map retrievalConvolutional autoencoderUnsupervised learning |
| spellingShingle | Heewoong Ahn Sunhwa Lee Hanseok Ko Meejoung Kim Sung Won Han Junhee Seok Searching similar weather maps using convolutional autoencoder and satellite images ICT Express Deep learning Weather map retrieval Convolutional autoencoder Unsupervised learning |
| title | Searching similar weather maps using convolutional autoencoder and satellite images |
| title_full | Searching similar weather maps using convolutional autoencoder and satellite images |
| title_fullStr | Searching similar weather maps using convolutional autoencoder and satellite images |
| title_full_unstemmed | Searching similar weather maps using convolutional autoencoder and satellite images |
| title_short | Searching similar weather maps using convolutional autoencoder and satellite images |
| title_sort | searching similar weather maps using convolutional autoencoder and satellite images |
| topic | Deep learning Weather map retrieval Convolutional autoencoder Unsupervised learning |
| url | http://www.sciencedirect.com/science/article/pii/S2405959522000546 |
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