FR2.P11.4
DETECTING CEMENT PLANTS WITH LANDSAT-8: A PHYSICS-INFORMED, MULTI-TEMPORAL, AND MULTI-SPECTRAL DEEP LEARNING FUSION APPROACH
Georgios Voulgaris, Maral Bayaraa, University of Oxford, United Kingdom; Cristian Rossi, European Space Agency, Netherlands
Session:
FR2.P11: Multisensor Data Fusion and Geospatial Data Intelligence I Oral
Track:
Community-Contributed Sessions
Location:
Room P11
Presentation Time:
Friday, 8 August, 11:15 - 11:30
Presentation
Discussion
Resources
No resources available.
Session FR2.P11
FR2.P11.1: Advancing High-Mountain Precipitation Reconstruction Through Merging of Multiple Data Sources: Triple Collocation Versus Signal-to-Noise Ratio Optimization
Suraj Shah, Yi Liu, University of New South Wales, Australia, Australia; Seokhyeon Kim, Kyung Hee University, Korea (South); Ashish Sharma, University of New South Wales, Australia
FR2.P11.2: Micro-Structures Graph-Based Point Cloud Registration for Balancing Efficiency and Accuracy
Rongling Zhang, Yan Li, Pengcheng Wei, Hong Xie, Pinzhuo Wang, Binbing Wang, Wuhan University, China
FR2.P11.3: A Novel Merging Framework for Generating High-Accuracy Global Soil Moisture by Error Decomposition Through Multiple Collocation Analysis
Xiaoxiao Min, Yulin Shangguan, Zhou Shi, Chinese Academy of Agricultural Sciences, Institute of Agricultural Resources and Regional Planning, China
FR2.P11.4: DETECTING CEMENT PLANTS WITH LANDSAT-8: A PHYSICS-INFORMED, MULTI-TEMPORAL, AND MULTI-SPECTRAL DEEP LEARNING FUSION APPROACH
Georgios Voulgaris, Maral Bayaraa, University of Oxford, United Kingdom; Cristian Rossi, European Space Agency, Netherlands
Resources
No resources available.