| 研究実績の概要 |
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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