TY - GEN
T1 - Deep Learning for Land Cover Mapping Using Sentinel-2 Imagery
T2 - 2023 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2023
AU - Salem, Muhammad
AU - Tsurusaki, Naoki
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Land cover mapping is essential for various applications, and the integration of satellite imagery and deep learning techniques offers an accurate and efficient solution. This study focuses on mapping land cover and change detection in the new extensions of Greater Cairo, Egypt, using Sentinel-2 imagery and convolutional neural networks (CNNs). The CNN model was trained on the BigEarthNet dataset, and transfer learning was applied using a pre-trained U-Net model. The results reveal significant land cover changes in Greater Cairo, particularly in the eastern region due to the construction of the New Administrative Capital. The accuracy assessment metrics, including precision, recall, and F1-score, demonstrate high accuracy levels exceeding 90%. These findings contribute to the advancement of land cover mapping and its applications in urban development.
AB - Land cover mapping is essential for various applications, and the integration of satellite imagery and deep learning techniques offers an accurate and efficient solution. This study focuses on mapping land cover and change detection in the new extensions of Greater Cairo, Egypt, using Sentinel-2 imagery and convolutional neural networks (CNNs). The CNN model was trained on the BigEarthNet dataset, and transfer learning was applied using a pre-trained U-Net model. The results reveal significant land cover changes in Greater Cairo, particularly in the eastern region due to the construction of the New Administrative Capital. The accuracy assessment metrics, including precision, recall, and F1-score, demonstrate high accuracy levels exceeding 90%. These findings contribute to the advancement of land cover mapping and its applications in urban development.
UR - https://www.scopus.com/pages/publications/85178343831
UR - https://www.scopus.com/pages/publications/85178343831#tab=citedBy
U2 - 10.1109/IGARSS52108.2023.10282957
DO - 10.1109/IGARSS52108.2023.10282957
M3 - Conference contribution
AN - SCOPUS:85178343831
T3 - International Geoscience and Remote Sensing Symposium (IGARSS)
SP - 6748
EP - 6751
BT - IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 16 July 2023 through 21 July 2023
ER -