Map of Land Cover Agreement: Ensambling Existing Datasets for Large-Scale Training Data Provision
Land cover information plays a critical role in supporting sustainable development and informed decision-making. Recent advancements in satellite data accessibility, computing power, and satellite technologies have boosted large-extent high-resolution land cover mapping. However, retrieving a suffic...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-07-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/15/15/3774 |
| _version_ | 1797586076716498944 |
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| author | Gorica Bratic Daniele Oxoli Maria Antonia Brovelli |
| author_facet | Gorica Bratic Daniele Oxoli Maria Antonia Brovelli |
| author_sort | Gorica Bratic |
| collection | DOAJ |
| description | Land cover information plays a critical role in supporting sustainable development and informed decision-making. Recent advancements in satellite data accessibility, computing power, and satellite technologies have boosted large-extent high-resolution land cover mapping. However, retrieving a sufficient amount of reliable training data for the production of such land cover maps is typically a demanding task, especially using modern deep learning classification techniques that require larger training sample sizes compared to traditional machine learning methods. In view of the above, this study developed a new benchmark dataset called the Map of Land Cover Agreement (MOLCA). MOLCA was created by integrating multiple existing high-resolution land cover datasets through a consensus-based approach. Covering Sub-Saharan Africa, the Amazon, and Siberia, this dataset encompasses approximately 117 billion 10m pixels across three macro-regions. The MOLCA legend aligns with most of the global high-resolution datasets and consists of nine distinct land cover classes. Noteworthy advantages of MOLCA include a higher number of pixels as well as coverage for typically underrepresented regions in terms of training data availability. With an estimated overall accuracy of 96%, MOLCA holds great potential as a valuable resource for the production of future high-resolution land cover maps. |
| first_indexed | 2024-03-11T00:17:12Z |
| format | Article |
| id | doaj.art-282c21a7d4f94394ae1778eb8a80be12 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-11T00:17:12Z |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-282c21a7d4f94394ae1778eb8a80be122023-11-18T23:30:37ZengMDPI AGRemote Sensing2072-42922023-07-011515377410.3390/rs15153774Map of Land Cover Agreement: Ensambling Existing Datasets for Large-Scale Training Data ProvisionGorica Bratic0Daniele Oxoli1Maria Antonia Brovelli2Department of Civil and Environmental Engineering, Politecnico di Milano, 20133 Milan, ItalyDepartment of Civil and Environmental Engineering, Politecnico di Milano, 20133 Milan, ItalyDepartment of Civil and Environmental Engineering, Politecnico di Milano, 20133 Milan, ItalyLand cover information plays a critical role in supporting sustainable development and informed decision-making. Recent advancements in satellite data accessibility, computing power, and satellite technologies have boosted large-extent high-resolution land cover mapping. However, retrieving a sufficient amount of reliable training data for the production of such land cover maps is typically a demanding task, especially using modern deep learning classification techniques that require larger training sample sizes compared to traditional machine learning methods. In view of the above, this study developed a new benchmark dataset called the Map of Land Cover Agreement (MOLCA). MOLCA was created by integrating multiple existing high-resolution land cover datasets through a consensus-based approach. Covering Sub-Saharan Africa, the Amazon, and Siberia, this dataset encompasses approximately 117 billion 10m pixels across three macro-regions. The MOLCA legend aligns with most of the global high-resolution datasets and consists of nine distinct land cover classes. Noteworthy advantages of MOLCA include a higher number of pixels as well as coverage for typically underrepresented regions in terms of training data availability. With an estimated overall accuracy of 96%, MOLCA holds great potential as a valuable resource for the production of future high-resolution land cover maps.https://www.mdpi.com/2072-4292/15/15/3774training datahigh-resolution land coverglobal land covermachine learningdeep learningsatellite image classification |
| spellingShingle | Gorica Bratic Daniele Oxoli Maria Antonia Brovelli Map of Land Cover Agreement: Ensambling Existing Datasets for Large-Scale Training Data Provision Remote Sensing training data high-resolution land cover global land cover machine learning deep learning satellite image classification |
| title | Map of Land Cover Agreement: Ensambling Existing Datasets for Large-Scale Training Data Provision |
| title_full | Map of Land Cover Agreement: Ensambling Existing Datasets for Large-Scale Training Data Provision |
| title_fullStr | Map of Land Cover Agreement: Ensambling Existing Datasets for Large-Scale Training Data Provision |
| title_full_unstemmed | Map of Land Cover Agreement: Ensambling Existing Datasets for Large-Scale Training Data Provision |
| title_short | Map of Land Cover Agreement: Ensambling Existing Datasets for Large-Scale Training Data Provision |
| title_sort | map of land cover agreement ensambling existing datasets for large scale training data provision |
| topic | training data high-resolution land cover global land cover machine learning deep learning satellite image classification |
| url | https://www.mdpi.com/2072-4292/15/15/3774 |
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