Self-Supervised Tree-level Biomass Estimation in Urban Environments From Airborne LiDAR and Optical Observations
arXiv:2606.26194v1 Announce Type: cross Abstract: Urban tree biomass remains less spatially explicitly quantified than biomass in managed forests because many estimates rely on inventories or coarse products that cannot resolve individual crowns or fine-scale heterogeneity. We present a crown-level above-ground biomass (AGB) framework for an 810~km$^2$ landscape in Ontario, Canada, using leaf-off airborne LiDAR (8--10~pulses~m$^{-2}$) and near-infrared RGB orthophotography (0.16--0.20~m) from 2018 and 2023. A dual-stream cross-attention network trained on rule-based pseudo-labels produced sema
The increasing availability of high-resolution remote sensing data and advanced AI techniques like self-supervised learning enables more precise large-scale environmental monitoring. This research is part of a broader trend towards leveraging AI for detailed environmental data analysis.
Accurate, scalable biomass estimation is crucial for carbon accounting, urban planning, and ecological research, offering a scientific basis for environmental policy and resource management. This level of detail in urban environments has previously been limited.
The ability to estimate individual tree-level biomass over large urban areas using AI and remote sensing improves the resolution and scalability of urban environmental monitoring. This moves beyond traditional inventory-based or coarse-grained methods.
- · Environmental monitoring technology providers
- · Urban planners and municipalities
- · Forestry and ecological researchers
- · AI and remote sensing companies
- · Traditional manual biomass inventory methods
- · Coarse-grained biomass estimation approaches
Improved understanding and quantification of urban carbon sequestration and biodiversity.
More effective urban greenspace management strategies and policies based on granular ecological data.
Potential integration into smart city infrastructure for dynamic environmental resource allocation and climate resilience initiatives.
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