دورية أكاديمية
A multi-source data fusion method for land cover production: a case study of the East European Plain
| العنوان: | A multi-source data fusion method for land cover production: a case study of the East European Plain |
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| المؤلفون: | Kai Li, Juanle Wang |
| المصدر: | International Journal of Digital Earth, Vol 17, Iss 1 (2024) |
| بيانات النشر: | Taylor & Francis Group, 2024. |
| سنة النشر: | 2024 |
| المجموعة: | LCC:Mathematical geography. Cartography |
| مصطلحات موضوعية: | Land cover, East European Plain, multi-source data fusion, deep learning, Mathematical geography. Cartography, GA1-1776 |
| الوصف: | ABSTRACTLarge-area high-precision land cover mapping faces challenges such as a lack of uniform classification systems and the inability to compare different products. The current use of deep learning methods in land cover data product generation provides opportunities to address these issues. However, this requires the creation of many manually labeled samples, and this involves high time and labor costs. Therefore, research is being conducted to examine methods for producing land cover products by integrating multiple data sources. This study focuses on the East European Plain and is based on land cover types that include water, forest, grass, wetland, crop, shrub, built area, bare area, ice, and tundra. Label images were fused using data from Dynamic World, ESA World Cover, ESRI Global LULC, GlobeLand30, and Open Land Map. Using a modified Dynamic World model, predictions for the East European Plain for 2022 were made, ultimately resulting in a land cover product at 10 m resolution. Compared to Dynamic World data, the classification system of this dataset aligns with the land cover conditions of the study area. The dataset possessed higher accuracy. This method integrates the advantages of existing data products, automates the generation of training labels, and effectively reduces manual costs. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 17538947 1753-8955 1753-8947 |
| Relation: | https://doaj.org/toc/1753-8947; https://doaj.org/toc/1753-8955 |
| DOI: | 10.1080/17538947.2024.2339360 |
| URL الوصول: | https://doaj.org/article/28f21161ac8f4542baa1e1d728ea1ba0 |
| رقم الأكسشن: | edsdoj.28f21161ac8f4542baa1e1d728ea1ba0 |
| قاعدة البيانات: | Directory of Open Access Journals |
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Full Text Finder | تدمد: | 17538947 17538955 |
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| DOI: | 10.1080/17538947.2024.2339360 |