TY - GEN
T1 - MCDD
T2 - 10th International Conference on Signal and Image Processing, ICSIP 2025
AU - Li, Qiutong
AU - Huang, Junqing
AU - Song, Zhilan
AU - Zhang, Jiaqi
AU - Yuan, Xiaochen
N1 - Publisher Copyright:
© 2025 IEEE.
PY - 2025
Y1 - 2025
N2 - Sea reclamation is essential for expanding land resources and supporting sustainable urban development in coastal areas. With the advancement of Remote Sensing (RS) technology and Convolutional Neural Network (CNN), sea reclamation land cover Change Detection (CD) has become feasible through high-resolution imagery analysis. However, there is a lack of publicly available large-scale sea reclamation datasets. Therefore, Macao Change Detection Dataset (MCDD) is developed, consisting of 10,000 256 256 resolution image pairs containing 15 years of land cover changes in Macao. This dataset provides valuable information on sea reclamation and lays the foundation for training and evaluating deep learning models for RS tasks. The performance of multiple state-of-the-art models on MCDD is evaluated in terms of IoU, Precision, Recall, F1, Kappa, Overall Accuracy (OA), parameters and Floating-point Operations (FLOPs). Experimental results demonstrate that MCDD is robust and effective for sea reclamation land cover CD.
AB - Sea reclamation is essential for expanding land resources and supporting sustainable urban development in coastal areas. With the advancement of Remote Sensing (RS) technology and Convolutional Neural Network (CNN), sea reclamation land cover Change Detection (CD) has become feasible through high-resolution imagery analysis. However, there is a lack of publicly available large-scale sea reclamation datasets. Therefore, Macao Change Detection Dataset (MCDD) is developed, consisting of 10,000 256 256 resolution image pairs containing 15 years of land cover changes in Macao. This dataset provides valuable information on sea reclamation and lays the foundation for training and evaluating deep learning models for RS tasks. The performance of multiple state-of-the-art models on MCDD is evaluated in terms of IoU, Precision, Recall, F1, Kappa, Overall Accuracy (OA), parameters and Floating-point Operations (FLOPs). Experimental results demonstrate that MCDD is robust and effective for sea reclamation land cover CD.
KW - convolutional neural network
KW - land cover change detection
KW - remote sensing technology
KW - sea reclamation
UR - https://www.scopus.com/pages/publications/105019492756
U2 - 10.1109/ICSIP65915.2025.11171523
DO - 10.1109/ICSIP65915.2025.11171523
M3 - Conference contribution
AN - SCOPUS:105019492756
T3 - 2025 10th International Conference on Signal and Image Processing, ICSIP 2025
SP - 227
EP - 231
BT - 2025 10th International Conference on Signal and Image Processing, ICSIP 2025
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 12 July 2025 through 14 July 2025
ER -