Frost forecasting via weakly-supervised semantic segmentation of satellite imagery
Recent research on frost forecasting has employed machine learning approaches to build prediction models tailored to specific locations. Although these models have proven effective, their applications are limited to areas where meteorological observations are available. Forecasting coverage can be e...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-12-01
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| Series: | GIScience & Remote Sensing |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/15481603.2025.2496013 |
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| author | Seokho Kang Seon-Kyeong Seong Eunha Sohn Jiyoung Kim Jaewoong Shim |
| author_facet | Seokho Kang Seon-Kyeong Seong Eunha Sohn Jiyoung Kim Jaewoong Shim |
| author_sort | Seokho Kang |
| collection | DOAJ |
| description | Recent research on frost forecasting has employed machine learning approaches to build prediction models tailored to specific locations. Although these models have proven effective, their applications are limited to areas where meteorological observations are available. Forecasting coverage can be extended by using meteorological satellite data as predictors, enabling frost forecasting over broader regions. This can be formulated as a semantic segmentation task of detecting areas where frost is likely to occur. Using satellite images and geographical information of the target region at the forecast time as inputs, the semantic segmentation model generates a frost probability map for the target time of forecast. However, an important challenge arises from the limited availability of pixel-wise labels, as frost occurrence information is only available for pixels corresponding to frost observatories. To address this issue, we propose a weakly-supervised learning method for training the semantic segmentation model using satellite imagery with incomplete supervision. The learning objective involves accurately classifying labeled pixels while suppressing the entire frost probability map to zero when no frost is observed at any observatory within the target region. Additionally, a metric-surrogate loss is incorporated to maximize the critical success index for labeled pixels. We demonstrate the effectiveness of the proposed method for frost forecasting with varying lead times across the South Korean region using Geo-KOMPSAT-2A satellite data. |
| format | Article |
| id | doaj-art-2e9be78d55c04a689c1d92c39f9d8437 |
| institution | Kabale University |
| issn | 1548-1603 1943-7226 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | GIScience & Remote Sensing |
| spelling | doaj-art-2e9be78d55c04a689c1d92c39f9d84372025-08-20T03:52:07ZengTaylor & Francis GroupGIScience & Remote Sensing1548-16031943-72262025-12-0162110.1080/15481603.2025.2496013Frost forecasting via weakly-supervised semantic segmentation of satellite imagerySeokho Kang0Seon-Kyeong Seong1Eunha Sohn2Jiyoung Kim3Jaewoong Shim4Department of Industrial Engineering, Sungkyunkwan University, Suwon, Republic of KoreaNational Meteorological Satellite Center, Korea Meteorological Administration, Jincheon, Republic of KoreaNational Meteorological Satellite Center, Korea Meteorological Administration, Jincheon, Republic of KoreaNational Meteorological Satellite Center, Korea Meteorological Administration, Jincheon, Republic of KoreaDepartment of Industrial Engineering, Seoul National University of Science and Technology, Seoul, Republic of KoreaRecent research on frost forecasting has employed machine learning approaches to build prediction models tailored to specific locations. Although these models have proven effective, their applications are limited to areas where meteorological observations are available. Forecasting coverage can be extended by using meteorological satellite data as predictors, enabling frost forecasting over broader regions. This can be formulated as a semantic segmentation task of detecting areas where frost is likely to occur. Using satellite images and geographical information of the target region at the forecast time as inputs, the semantic segmentation model generates a frost probability map for the target time of forecast. However, an important challenge arises from the limited availability of pixel-wise labels, as frost occurrence information is only available for pixels corresponding to frost observatories. To address this issue, we propose a weakly-supervised learning method for training the semantic segmentation model using satellite imagery with incomplete supervision. The learning objective involves accurately classifying labeled pixels while suppressing the entire frost probability map to zero when no frost is observed at any observatory within the target region. Additionally, a metric-surrogate loss is incorporated to maximize the critical success index for labeled pixels. We demonstrate the effectiveness of the proposed method for frost forecasting with varying lead times across the South Korean region using Geo-KOMPSAT-2A satellite data.https://www.tandfonline.com/doi/10.1080/15481603.2025.2496013Frost forecastingmeteorological satellitesemantic segmentationweakly-supervised learningconvolutional neural network |
| spellingShingle | Seokho Kang Seon-Kyeong Seong Eunha Sohn Jiyoung Kim Jaewoong Shim Frost forecasting via weakly-supervised semantic segmentation of satellite imagery GIScience & Remote Sensing Frost forecasting meteorological satellite semantic segmentation weakly-supervised learning convolutional neural network |
| title | Frost forecasting via weakly-supervised semantic segmentation of satellite imagery |
| title_full | Frost forecasting via weakly-supervised semantic segmentation of satellite imagery |
| title_fullStr | Frost forecasting via weakly-supervised semantic segmentation of satellite imagery |
| title_full_unstemmed | Frost forecasting via weakly-supervised semantic segmentation of satellite imagery |
| title_short | Frost forecasting via weakly-supervised semantic segmentation of satellite imagery |
| title_sort | frost forecasting via weakly supervised semantic segmentation of satellite imagery |
| topic | Frost forecasting meteorological satellite semantic segmentation weakly-supervised learning convolutional neural network |
| url | https://www.tandfonline.com/doi/10.1080/15481603.2025.2496013 |
| work_keys_str_mv | AT seokhokang frostforecastingviaweaklysupervisedsemanticsegmentationofsatelliteimagery AT seonkyeongseong frostforecastingviaweaklysupervisedsemanticsegmentationofsatelliteimagery AT eunhasohn frostforecastingviaweaklysupervisedsemanticsegmentationofsatelliteimagery AT jiyoungkim frostforecastingviaweaklysupervisedsemanticsegmentationofsatelliteimagery AT jaewoongshim frostforecastingviaweaklysupervisedsemanticsegmentationofsatelliteimagery |