| A densely attentive refinement network for change detection based on very-high-resolution bitemporal remote sensing images Z Li, C Yan, Y Sun, Q Xin IEEE Transactions on Geoscience and Remote Sensing 60, 1-18, 2022 | 62 | 2022 |
| Evaluations and comparisons of rule-based and machine-learning-based methods to retrieve satellite-based vegetation phenology using MODIS and USA National Phenology Network data Q Xin, J Li, Z Li, Y Li, X Zhou International Journal of Applied Earth Observation and Geoinformation 93, 102189, 2020 | 48 | 2020 |
| Counting trees in a subtropical mega city using the instance segmentation method Y Sun, Z Li, H He, L Guo, X Zhang, Q Xin International Journal of Applied Earth Observation and Geoinformation 106 …, 2022 | 29 | 2022 |
| A deep learning-based framework for automated extraction of building footprint polygons from very high-resolution aerial imagery Z Li, Q Xin, Y Sun, M Cao Remote Sensing 13 (18), 3630, 2021 | 27 | 2021 |
| Identifying leaf phenology of deciduous broadleaf forests from phenocam images using a convolutional neural network regression method M Cao, Y Sun, X Jiang, Z Li, Q Xin Remote Sensing 13 (12), 2331, 2021 | 14 | 2021 |
| High-resolution mapping of paddy rice fields from unmanned airborne vehicle images using enhanced-TransUnet C Yan, Z Li, Z Zhang, Y Sun, Y Wang, Q Xin Computers and Electronics in Agriculture 210, 107867, 2023 | 7 | 2023 |
| Developing global annual land surface phenology datasets (1982–2018) from the AVHRR data using multiple phenology retrieval methods W Wu, Z Li, Z Zhang, C Yan, K Xiao, Y Wang, Q Xin Ecological Indicators 150, 110262, 2023 | 2 | 2023 |
Qinchuan XinSun Yat-sen UniversityVerified email at mail.sysu.edu.cn
