3 citations · 5 across the 4 of their papers we have counts for
4 papers
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…
UFLUX v2.0: A Process-Informed Machine Learning Framework for Efficient and Explainable Modelling of Terrestrial Carbon Uptake
Wenquan Dong, Songyan Zhu, Jian Xu +6
Gross Primary Productivity (GPP), the amount of carbon plants fixed by photosynthesis, is pivotal for understanding the global carbon cycle and ecosystem functioning. Process-based…
Multimodal deep learning for mapping forest dominant height by fusing GEDI with earth observation data
Man Chen, Wenquan Dong, Hao Yu +5
The integration of multisource remote sensing data and deep learning models offers new possibilities for accurately mapping high spatial resolution forest height. We found that GED…
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…