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Multimodal deep learning for mapping forest dominant height by fusing GEDI with earth observation data
Consequently, we proposed a novel deep learning framework termed the multi-modal attention remote sensing network (MARSNet) to estimate forest dominant height by extrapolating dominant height derived from GEDI, using Setinel-1 data, ALOS-2 PALSAR-2 data, Sentinel-2 optical data and ancillary data.
Forest aboveground biomass estimation using GEDI and earth observation data through attention-based deep learning
AU-FC achieved intermediate R2 of 0. 64, RMSE of 44. 92 Mgha-1, and bias of -0. 56 Mg ha-1, outperforming RF but underperforming AU model using spatial information.
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