arXiv Machine Learning July 31, 2026 By Shashika Lamahewage, Chandi Witharana

Foundation-Model Earth Representations Enable Regional-Scale Forest Aboveground Biomass Monitoring Across the Northeastern United States

Read the original on arXiv Machine Learning →

arXiv:2607. 27217v1 Announce Type: cross Abstract: Forest aboveground biomass (AGB) is a critical indicator of ecosystem productivity and terrestrial carbon storage, yet regional carbon monitoring remains constrained by the sparse spatial and temporal availability of field inventories and airborne structural measurements.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv Machine Learning

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Uncertainty Quantification for EO Regression Tasks: Building Height, Tree Canopy Height and Above-ground Biomass Estimation

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Above-ground Biomass Estimation with Geospatial Foundation Models

FLORA: A deep learning approach to predict forest attributes from heterogeneous LiDAR data

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