A Scoping Review of Automated Calving Front Detection in Satellite Images and Calving Front Position Datasets.
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| Title: | A Scoping Review of Automated Calving Front Detection in Satellite Images and Calving Front Position Datasets. |
|---|---|
| Authors: | Milczarek, Wojciech1 (AUTHOR), Sompolski, Marek1 (AUTHOR) marek.sompolski@pwr.edu.pl, Tympalski, Michał1 (AUTHOR), Kopeć, Anna1 (AUTHOR) |
| Source: | Remote Sensing. Apr2026, Vol. 18 Issue 7, p969. 38p. |
| Subjects: | Convolutional neural networks, Model validation, Ice mechanics, Machine learning, Image analysis, Remote-sensing images, Deep learning, Calibration |
| Abstract: | Highlights: What are the main findings? Despite significant algorithmic improvements and methodological shifts in automated deep learning models, they have not yet matched the precision of human interpreters. Analysis of the spatial distribution of studied glaciers reveals a strong regional bias in current data. What are the implications of the main findings? The methodological shift allows for processing vast amounts of satellite data that would be unfeasible to handle manually, but the accuracy gap implies that human experts are still required for high-precision studies and to validate model outputs. The regional bias in current data implies a need to develop benchmarking datasets that include images from Arctic regions outside of Greenland and Svalbard to ensure model transferability and robustness. Calving front position is a key indicator of glacier and ice-sheet dynamics and an important variable for assessing mass loss and sea-level rise. Rapid growth in satellite data availability and image analysis techniques has driven the development of numerous automated calving front detection algorithms; however, the methodological landscape remains fragmented. This scoping review aims to map the existing literature on automated calving front detection, characterize the types of algorithms and data sources used, and identify trends, gaps, and challenges in current approaches. A systematic search of major bibliographic databases and complementary sources was conducted to identify studies describing automated or semi-automated calving front detection from satellite imagery or derived datasets. Eligible studies included peer-reviewed articles and relevant grey literature using optical, synthetic aperture radar (SAR), or multi-sensor data. Data were charted using a predefined framework that captures the algorithmic approach, input data characteristics, spatial and temporal coverage, validation strategies, and reported performance metrics. The review identifies a wide range of methods, from early threshold- and edge-based techniques to recent machine learning and deep learning approaches, with a strong shift toward convolutional neural networks over the past few years. Despite methodological progress, validation practices and evaluation metrics remain heterogeneous, and standardized benchmark datasets are scarce. This scoping review provides a structured overview of the field and highlights priorities for future methodological development and benchmarking. [ABSTRACT FROM AUTHOR] |
| Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 192958504 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: A Scoping Review of Automated Calving Front Detection in Satellite Images and Calving Front Position Datasets. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Milczarek%2C+Wojciech%22">Milczarek, Wojciech</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Sompolski%2C+Marek%22">Sompolski, Marek</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> marek.sompolski@pwr.edu.pl</i><br /><searchLink fieldCode="AR" term="%22Tympalski%2C+Michał%22">Tympalski, Michał</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Kopeć%2C+Anna%22">Kopeć, Anna</searchLink><relatesTo>1</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Remote+Sensing%22">Remote Sensing</searchLink>. Apr2026, Vol. 18 Issue 7, p969. 38p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Convolutional+neural+networks%22">Convolutional neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Model+validation%22">Model validation</searchLink><br /><searchLink fieldCode="DE" term="%22Ice+mechanics%22">Ice mechanics</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Image+analysis%22">Image analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Remote-sensing+images%22">Remote-sensing images</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Calibration%22">Calibration</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Highlights: What are the main findings? Despite significant algorithmic improvements and methodological shifts in automated deep learning models, they have not yet matched the precision of human interpreters. Analysis of the spatial distribution of studied glaciers reveals a strong regional bias in current data. What are the implications of the main findings? The methodological shift allows for processing vast amounts of satellite data that would be unfeasible to handle manually, but the accuracy gap implies that human experts are still required for high-precision studies and to validate model outputs. The regional bias in current data implies a need to develop benchmarking datasets that include images from Arctic regions outside of Greenland and Svalbard to ensure model transferability and robustness. Calving front position is a key indicator of glacier and ice-sheet dynamics and an important variable for assessing mass loss and sea-level rise. Rapid growth in satellite data availability and image analysis techniques has driven the development of numerous automated calving front detection algorithms; however, the methodological landscape remains fragmented. This scoping review aims to map the existing literature on automated calving front detection, characterize the types of algorithms and data sources used, and identify trends, gaps, and challenges in current approaches. A systematic search of major bibliographic databases and complementary sources was conducted to identify studies describing automated or semi-automated calving front detection from satellite imagery or derived datasets. Eligible studies included peer-reviewed articles and relevant grey literature using optical, synthetic aperture radar (SAR), or multi-sensor data. Data were charted using a predefined framework that captures the algorithmic approach, input data characteristics, spatial and temporal coverage, validation strategies, and reported performance metrics. The review identifies a wide range of methods, from early threshold- and edge-based techniques to recent machine learning and deep learning approaches, with a strong shift toward convolutional neural networks over the past few years. Despite methodological progress, validation practices and evaluation metrics remain heterogeneous, and standardized benchmark datasets are scarce. This scoping review provides a structured overview of the field and highlights priorities for future methodological development and benchmarking. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/rs18070969 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 38 StartPage: 969 Subjects: – SubjectFull: Convolutional neural networks Type: general – SubjectFull: Model validation Type: general – SubjectFull: Ice mechanics Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Image analysis Type: general – SubjectFull: Remote-sensing images Type: general – SubjectFull: Deep learning Type: general – SubjectFull: Calibration Type: general Titles: – TitleFull: A Scoping Review of Automated Calving Front Detection in Satellite Images and Calving Front Position Datasets. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Milczarek, Wojciech – PersonEntity: Name: NameFull: Sompolski, Marek – PersonEntity: Name: NameFull: Tympalski, Michał – PersonEntity: Name: NameFull: Kopeć, Anna IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 04 Text: Apr2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 20724292 Numbering: – Type: volume Value: 18 – Type: issue Value: 7 Titles: – TitleFull: Remote Sensing Type: main |
| ResultId | 1 |
