2023 Fiscal Year Annual Research Report
Monitoring and Prediction of Land Use and Land Cover Change Using Remote Sensing and De ep Learning
| Project/Area Number |
22KF0301
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| Allocation Type | Multi-year Fund |
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| Research Institution | Kyushu University |
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Principal Investigator |
鶴崎 直樹 九州大学, 人間環境学研究院, 准教授 (20264096)
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| Co-Investigator(Kenkyū-buntansha) |
MUHAMMAD MUHAMMAD 九州大学, 人間環境学研究院, 外国人特別研究員
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| Project Period (FY) |
2023-03-08 – 2024-03-31
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| Keywords | Land Use / Land Cover / deep learning / satellite imagery |
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| Outline of Annual Research Achievements |
In this study, we used Sentinel-2 satellite images (high-resolution multispectral data) from 2015 to 2021 taken by the European Space Agency (ESA) to detect land cover conditions and changes. We also utilized convolutional neural network (CNN) models based on deep learning (DL) techniques.
The objective of this study was to demonstrate the versatility and effectiveness of this method in addressing the challenges of land cover mapping across different contexts and landscapes in the earthquake affected areas of Mashiki Town, Kumamoto Prefecture and Greater Cairo, Egypt.
The innovative approach of combining deep learning and remote sensing techniques in this study proved to be very effective in tackling complex urban dynamics. A particularly striking achievement is the exceptional accuracy level achieved by the trained model. Across all land use and land cover classes in the study area, our model consistently achieved accuracy levels in excess of 90%.
This high level of accuracy demonstrates the robustness and reliability of our approach in accurately classifying and mapping land use and land cover change. We have utilized geospatial metrics and remote sensing methods to delve deeper into the dynamics of urban landscapes and provide a more comprehensive understanding of spatial change over time.
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