3 citations · 5 across the 4 of their papers we have counts for
4 papers
StruMPL: Multi-task Dense Regression under Disjoint Partial Supervision and MNAR Labels
Reza M. Asiyabi, Juan Alberto Molina-Valero, The SEOSAW Partnership +2
Estimating forest aboveground biomass (AGB) from Earth observation combines two structurally incompatible label sources: spaceborne lidar provides canopy structure at millions of l…
Process-Guided Concept Bottleneck Model
Reza M. Asiyabi, SEOSAW Partnership, Steven Hancock +1
Concept Bottleneck Models (CBMs) improve the explainability of black-box Deep Learning (DL) by introducing intermediate semantic concepts. However, standard CBMs often overlook dom…
Comparing remote sensing-based forest biomass mapping approaches using new forest inventory plots in contrasting forests in northeastern and southwestern China
Wenquan Dong, Edward T. A. Mitchard, Yuwei Chen +5
Large-scale high spatial resolution aboveground biomass (AGB) maps play a crucial role in determining forest carbon stocks and how they are changing, which is instrumental in under…
Forest aboveground biomass estimation using GEDI and earth observation data through attention-based deep learning
Wenquan Dong, Edward T. A. Mitchard, Hao Yu +2
Accurate quantification of forest aboveground biomass (AGB) is critical for understanding carbon accounting in the context of climate change. In this study, we presented a novel at…