Assessment of Machine Learning Algorithms for Land Cover Classification Using Remotely Sensed Data Article Swipe
Jeongmook Park
,
Yong Kyu Lee
,
Jungsoo Lee
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.18494/sam.2021.3612
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.18494/sam.2021.3612
The purpose of this study was to apply the random forest (RF), XGBoost, and LightGBM machine learning (ML) algorithms to land cover classification, and to present the model tuning process for each algorithm.Sentinel-2 satellite images were used for land cover classification, and the land cover map provided by the Ministry of Environment of the Republic of Korea was used as label data.Each ML algorithm was applied using the constructed dataset.In addition, each ML algorithm was optimized by three methods (grid search, random search, and Bayesian optimization).The grid search took the longest time to optimize the hyperparameters because it required the highest number of search iterations, but the accuracy was highest.The random search was the fastest method of optimizing the hyperparameters.The accuracy of XGBoost was the highest for each ML algorithm.The prediction of XGBoost was the most consistent with the land cover map provided by the Ministry of Environment.However, the LightGBM algorithm has a major advantage in terms of the algorithm optimization and application time.Therefore, our study is meaningful in that we obtained a higher accuracy and shorter time for each ML algorithm.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.18494/sam.2021.3612
- https://sensors.myu-group.co.jp/sm_pdf/SM2731.pdf
- OA Status
- gold
- Cited By
- 15
- References
- 36
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3213960087
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3213960087Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.18494/sam.2021.3612Digital Object Identifier
- Title
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Assessment of Machine Learning Algorithms for Land Cover Classification Using Remotely Sensed DataWork 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
- Publication date
-
2021-11-17Full publication date if available
- Authors
-
Jeongmook Park, Yong Kyu Lee, Jungsoo LeeList of authors in order
- Landing page
-
https://doi.org/10.18494/sam.2021.3612Publisher landing page
- PDF URL
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https://sensors.myu-group.co.jp/sm_pdf/SM2731.pdfDirect link to full text PDF
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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://sensors.myu-group.co.jp/sm_pdf/SM2731.pdfDirect OA link when available
- Concepts
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Land cover, Cover (algebra), Computer science, Remote sensing, Artificial intelligence, Machine learning, Data mining, Pattern recognition (psychology), Land use, Engineering, Geography, Civil engineering, Mechanical engineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
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15Total citation count in OpenAlex
- Citations by year (recent)
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2025: 4, 2024: 5, 2023: 4, 2022: 2Per-year citation counts (last 5 years)
- References (count)
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36Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.terms | 156 |
| abstract_inverted_index.three | 77 |
| abstract_inverted_index.using | 66 |
| abstract_inverted_index.forest | 10 |
| abstract_inverted_index.higher | 173 |
| abstract_inverted_index.images | 34 |
| abstract_inverted_index.method | 115 |
| abstract_inverted_index.number | 101 |
| abstract_inverted_index.random | 9, 81, 110 |
| abstract_inverted_index.search | 87, 103, 111 |
| abstract_inverted_index.tuning | 28 |
| abstract_inverted_index.XGBoost | 122, 132 |
| abstract_inverted_index.applied | 65 |
| abstract_inverted_index.because | 96 |
| abstract_inverted_index.fastest | 114 |
| abstract_inverted_index.highest | 100, 125 |
| abstract_inverted_index.longest | 90 |
| abstract_inverted_index.machine | 15 |
| abstract_inverted_index.methods | 78 |
| abstract_inverted_index.present | 25 |
| abstract_inverted_index.process | 29 |
| abstract_inverted_index.purpose | 1 |
| abstract_inverted_index.search, | 80, 82 |
| abstract_inverted_index.shorter | 176 |
| abstract_inverted_index.Bayesian | 84 |
| abstract_inverted_index.LightGBM | 14, 149 |
| abstract_inverted_index.Ministry | 49, 145 |
| abstract_inverted_index.Republic | 54 |
| abstract_inverted_index.XGBoost, | 12 |
| abstract_inverted_index.accuracy | 107, 120, 174 |
| abstract_inverted_index.learning | 16 |
| abstract_inverted_index.obtained | 171 |
| abstract_inverted_index.optimize | 93 |
| abstract_inverted_index.provided | 46, 142 |
| abstract_inverted_index.required | 98 |
| abstract_inverted_index.addition, | 70 |
| abstract_inverted_index.advantage | 154 |
| abstract_inverted_index.algorithm | 63, 73, 150, 159 |
| abstract_inverted_index.data.Each | 61 |
| abstract_inverted_index.optimized | 75 |
| abstract_inverted_index.satellite | 33 |
| abstract_inverted_index.algorithm. | 181 |
| abstract_inverted_index.algorithms | 18 |
| abstract_inverted_index.consistent | 136 |
| abstract_inverted_index.dataset.In | 69 |
| abstract_inverted_index.meaningful | 167 |
| abstract_inverted_index.optimizing | 117 |
| abstract_inverted_index.prediction | 130 |
| abstract_inverted_index.Environment | 51 |
| abstract_inverted_index.application | 162 |
| abstract_inverted_index.constructed | 68 |
| abstract_inverted_index.highest.The | 109 |
| abstract_inverted_index.iterations, | 104 |
| abstract_inverted_index.optimization | 160 |
| abstract_inverted_index.algorithm.The | 129 |
| abstract_inverted_index.classification, | 22, 40 |
| abstract_inverted_index.hyperparameters | 95 |
| abstract_inverted_index.time.Therefore, | 163 |
| abstract_inverted_index.optimization).The | 85 |
| abstract_inverted_index.hyperparameters.The | 119 |
| abstract_inverted_index.Environment.However, | 147 |
| abstract_inverted_index.algorithm.Sentinel-2 | 32 |
| cited_by_percentile_year.max | 98 |
| cited_by_percentile_year.min | 94 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 3 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/13 |
| sustainable_development_goals[0].score | 0.5199999809265137 |
| sustainable_development_goals[0].display_name | Climate action |
| citation_normalized_percentile.value | 0.85361595 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |