• 2019.09-now              Ph. D. in School of Geography and Planning, Sun Yat-sen University.         Supervisor: Prof. Qian Shi
• 2015.09-2019.06         B.S. in School of Geography and Planning, Sun Yat-sen University.

[12] Q. Shi, M. Liu (*Corresponding author), A. Marinoni and X. Liu, “UGS-1m: Fine-grained urban green space mapping of 31 major cities in China based on the deep learning framework,” Earth System Science Data(ESSD), 15, 555–577, 2023. (SCI Q1 Top, IF=11.815) [Paper] [Project]

[11] M. Liu, H. Deng and W. Dong, “Identification of Mangrove Invasive Plant Derris trifoliate using UAV Images and Deep Learning Algorithms,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (JSTARS), 2022. (SCI Q1, IF=4.715) [Paper]

[10] M. Liu, Q. Shi, J. Li and Z. Chai, “Learning Token-aligned Representations with Multi-model Transformers for Different-resolution Change Detection,” IEEE Transactions on Geoscience and Remote Sensing (TGRS), 2022. (SCI Q1 Top, IF=8.125) [Paper] [Code]

[09] M. Liu, Q. Shi, Z. Chai and J. Li, “PA-Former: Learning Prior-aware Transformer for Remote Sensing Building Change Detection,” IEEE Geoscience and Remote Sensing Letters (GRSL), 2022. (SCI Q1, IF=5.343) [Paper] [Code]

[08] H. Li, F. Zhu, X. Zheng, M. Liu, and G. Chen, “MSCDUNet: A Deep Learning Framework for Built-Up Area Change Detection Integrating Multispectral, SAR, and VHR Data,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (JSTARS), vol. 15, pp. 5163-5176, 2022. (SCI Q1, IF=4.715) [Paper]

[07] M. Liu, Z. Chai, H. Deng and R. Liu, “A CNN-Transformer Network With Multiscale Context Aggregation for Fine-Grained Cropland Change Detection,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (JSTARS), vol. 15, pp. 4297-4306, 2022. (SCI Q1, IF=4.715) [Paper] [Code]

[06] M. Liu, Q. Shi, A. Marinoni, D. He, X. Liu and L. Zhang, “Super-Resolution-Based Change Detection Network With Stacked Attention Module for Images With Different Resolutions,” IEEE Transactions on Geoscience and Remote Sensing (TGRS), 2021, Art no. 4403718. (SCI Q1 Top, IF=8.125) [Paper] [Code]

[05] Q. Shi, M. Liu (*Corresponding author), S. Li, X. Liu, F. Wang and L. Zhang, “A Deeply Supervised Attention Metric-Based Network and an Open Aerial Image Dataset for Remote Sensing Change Detection,” IEEE Transactions on Geoscience and Remote Sensing (TGRS), 2021, Art no. 5604816. (SCI Q1 Top, IF=8.125) [Paper] [Dataset] [Code]

[04] M. Liu, P. Zhang, Q. Shi and M. Liu, “An Adversarial Domain Adaptation Framework With KL-Constraint for Remote Sensing Land Cover Classification,” IEEE Geoscience and Remote Sensing Letters (GRSL), 2021, Art no. 3002305. (SCI Q1, IF=5.343) [Paper]

[03] X. Dou, C. Li, Q. Shi, M. Liu, “Super‑Resolution for Hyperspectral Remote Sensing Images Based on the 3D Attention‑SRGAN Network,” Remote Sensing, vol. 12, pp. 1204, 2020. (SCI Q1, IF=5.349) [Paper]

[02] Q. Shi, M. Liu, X. Liu, P. Liu, P. Zhang, J. Yang and X. Li, “Domain Adaption for Fine-Grained Urban Village Extraction From Satellite Images,” IEEE Geoscience and Remote Sensing Letters (GRSL), vol. 17, pp. 1430-1434, 2020. (SCI Q1, IF=5.343) [Paper]

[01] P. Liu, X. Liu, M. Liu, Q. Shi, J. Yang, X. Xu and Y. Zhang, “Building Footprint Extraction from High-Resolution Images via Spatial Residual Inception Convolutional Neural Network,” Remote Sensing, vol. 11, pp. 830, 2019. (SCI Q1, IF=5.349) [Paper]

[03] M. Liu, J. Li, Z. Li and Q. Shi, “A Deep Learning Method for Fined-Grained Urban Green Space Mapping,” Proceeding of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS), in Kuala Lumpur, Malaysia, pp. 6029-6032, 2022. (EI, Oral) [Paper]

[02] M. Liu and Q. Shi, “DSAMNet: A Deeply Supervised Attention Metric Based Network for Change Detection of High-Resolution Images,” Proceeding of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS), in Brussels, Belgium, pp. 6159-6162, 2021. (EI, Oral) [Paper]

[01] M. Liu, Q. Shi, P. Liu and C. Wan, “Siamese Generative Adversarial Network for Change Detection Under Different Scales,” Proceeding of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS), in Hawaii, USA, pp. 2543-2546, 2020. (EI, Oral) [Paper]

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