MO4.P6.2
FARMLAND CHANGE MONITORING USING MULTI-TEMPORAL SAR DATA WITH DEEP LEARNING
Chaowei Jiang, Chao Wang, Fan Wu, Lu Xu, Nan Chen, Tianyang Li, Yixian Tang, Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, China
Session:
MO4.P6: Advances in Machine Learning for Agricultural Land Use and Land Cover Classification II Oral
Track:
Community-Contributed Sessions
Location:
Room P6
Presentation Time:
Monday, 4 August, 16:00 - 16:15
Session Co-Chairs:
Kathryn Sheffield, Department of Energy, Environment and Climate Action and Sabah Sabaghy,
Presentation
Discussion
Resources
No resources available.
Session MO4.P6
MO4.P6.1: Automated In-Season Crop-Type Data Layer Mapping Without Ground Truth for the Conterminous United States Based on Multisource Satellite Imagery
Hui Li, Liping Di, Chen Zhang, Li Lin, George Mason Universitty, United States; Liying Guo, George Mason University, United States; Eugene Yu, George Mason Universitty, United States; Zhengwei Yang, U.S. Department of Agriculture, United States
MO4.P6.2: FARMLAND CHANGE MONITORING USING MULTI-TEMPORAL SAR DATA WITH DEEP LEARNING
Chaowei Jiang, Chao Wang, Fan Wu, Lu Xu, Nan Chen, Tianyang Li, Yixian Tang, Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, China
MO4.P6.3: Long-term (2013–2022) mapping of winter wheat in the North China Plain using Landsat data: classification with optimal zoning strategy
Yifei Liu, Xuehong Chen, Jin Chen, Yunze Zang, Beijing Normal University, China; Jingyi Wang, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, China; Miao Lu, Liang Sun, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, China; Qi Dong, Beijing Normal University, China; Bingwen Qiu, Fuzhou University, China; Xiufang Zhu, Beijing Normal University, China
Resources
No resources available.