arXiv:2603. 23669v2 Announce Type: replace-cross Abstract: Accurate estimation of forest biomass, a major carbon sink, relies heavily on tree-level traits such as height and species.
Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.
arXiv:2606. 20291v1 Announce Type: new Abstract: Remote sensing is increasingly relied upon to deliver actionable science for forest and wildfire risk management across large landscapes.
arXiv:2510. 09458v2 Announce Type: replace-cross Abstract: Interest in forestry automation is growing alongside rapid advances in deep learning.
arXiv:2606. 32023v1 Announce Type: cross Abstract: Forest attributes are essential for national-scale resource monitoring.
arXiv:2606. 14562v1 Announce Type: cross Abstract: Sociable weaver nests function as complex ecological structures offering thermoregulatory microhabitats and sustaining diverse species; however, datasets used in prior studies lack fine-grained 3D structural detail.
Over the past decade, interest in applying machine learning (ML) to automate forest monitoring has grown significantly. However, existing training datasets are predominantly drawn from North America, Europe, Asia, and Australia, leaving a critical gap in African forestry data.
arXiv:2608. 06406v1 Announce Type: cross Abstract: Accurate estimation of forest height from satellite imagery is essential for applications such as carbon accounting, biodiversity monitoring, and ecosystem management.