Machine learning approaches to Landsat change detection analysis
The Landsat mission has captured images of the Earth’s surface for over 50 years, and the data have enabled researchers to investigate a vast array of different change phenomena using machine learning models. Landsat-based monitoring research has been influential in geography, forestry, hydrology, e...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-12-01
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| Series: | Canadian Journal of Remote Sensing |
| Online Access: | http://dx.doi.org/10.1080/07038992.2024.2448169 |
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| _version_ | 1832593475078455296 |
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| author | Galen Richardson Anders Knudby Morgan A. Crowley Michael Sawada Wenjun Chen |
| author_facet | Galen Richardson Anders Knudby Morgan A. Crowley Michael Sawada Wenjun Chen |
| author_sort | Galen Richardson |
| collection | DOAJ |
| description | The Landsat mission has captured images of the Earth’s surface for over 50 years, and the data have enabled researchers to investigate a vast array of different change phenomena using machine learning models. Landsat-based monitoring research has been influential in geography, forestry, hydrology, ecology, agriculture, geology, and public health. When monitoring Earth’s surface change using Landsat data and machine learning, it is essential to consider the implications of the size of the study area, specifics of the machine learning model, and image temporal density. We found that there are two general approaches to Landsat change detection analysis with machine learning: post-classification comparison and sequential imagery stack approaches. The two approaches have different advantages, and the design of an appropriate type of Landsat change detection analysis depends on the task at hand and the available computing resources. This review provides an overview of different Landsat change detection approaches using machine learning, outlines a framework for understanding the relevant considerations, and discusses recent developments such as generative artificial intelligence, explainable machine learning, and ethical analysis considerations. |
| format | Article |
| id | doaj-art-704ea2d2fcc94ece89f30421343362e2 |
| institution | Kabale University |
| issn | 1712-7971 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Canadian Journal of Remote Sensing |
| spelling | doaj-art-704ea2d2fcc94ece89f30421343362e22025-01-20T14:37:59ZengTaylor & Francis GroupCanadian Journal of Remote Sensing1712-79712025-12-0151110.1080/07038992.2024.24481692448169Machine learning approaches to Landsat change detection analysisGalen Richardson0Anders Knudby1Morgan A. Crowley2Michael Sawada3Wenjun Chen4Department of Geography, Environment and Geomatics, University of OttawaDepartment of Geography, Environment and Geomatics, University of OttawaCanadian Forest Service (Great Lakes Forestry Centre), Natural Resources CanadaDepartment of Geography, Environment and Geomatics, University of OttawaCanada Centre for Mapping and Earth Observation, Natural Resources CanadaThe Landsat mission has captured images of the Earth’s surface for over 50 years, and the data have enabled researchers to investigate a vast array of different change phenomena using machine learning models. Landsat-based monitoring research has been influential in geography, forestry, hydrology, ecology, agriculture, geology, and public health. When monitoring Earth’s surface change using Landsat data and machine learning, it is essential to consider the implications of the size of the study area, specifics of the machine learning model, and image temporal density. We found that there are two general approaches to Landsat change detection analysis with machine learning: post-classification comparison and sequential imagery stack approaches. The two approaches have different advantages, and the design of an appropriate type of Landsat change detection analysis depends on the task at hand and the available computing resources. This review provides an overview of different Landsat change detection approaches using machine learning, outlines a framework for understanding the relevant considerations, and discusses recent developments such as generative artificial intelligence, explainable machine learning, and ethical analysis considerations.http://dx.doi.org/10.1080/07038992.2024.2448169 |
| spellingShingle | Galen Richardson Anders Knudby Morgan A. Crowley Michael Sawada Wenjun Chen Machine learning approaches to Landsat change detection analysis Canadian Journal of Remote Sensing |
| title | Machine learning approaches to Landsat change detection analysis |
| title_full | Machine learning approaches to Landsat change detection analysis |
| title_fullStr | Machine learning approaches to Landsat change detection analysis |
| title_full_unstemmed | Machine learning approaches to Landsat change detection analysis |
| title_short | Machine learning approaches to Landsat change detection analysis |
| title_sort | machine learning approaches to landsat change detection analysis |
| url | http://dx.doi.org/10.1080/07038992.2024.2448169 |
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