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This research applies Attentive Neural Processes, a probabilistic meta-learning framework, to improve biomass density estimation from NASA's GEDI LIDAR data across diverse ecosystems. The study demonstrates how adaptive uncertainty quantification can outperform standard ensemble methods like Random Forests by accounting for landscape heterogeneity. This represents a significant advancement in applying advanced machine learning architectures to large-scale geospatial inference problems.
Interpolation of GEDI Biomass Estimates with Calibrated Uncertainty Quantification
arXiv:2601.16834v4 Announce Type: replace Abstract: Wall-to-wall biomass density estimation from NASA's GEDI mission requires interpolating sparse LIDAR observations across heterogeneous landscapes. While Random Forest and XGBoost are widely used, they predict each location independently from satellite features, without adapting to the varying difficulty of heterogeneous landscapes. We introduce Attentive Neural Processes (ANPs), a probabilistic meta-learning architecture that explicitly conditions predictions on local observation sets as well as geospatial foundation model embeddings. ANPs learn a flexible spatial covariance approximator that is more uncertain in complex landscapes and less in homogeneous areas. We validate this approach across five distinct biomes ranging from tropical Amazonian forests to boreal, temperate, and alpine ecosystems, demonstrating that ANPs achieve competitive accuracy while maintaining uncertainty calibration. We demonstrate a scalable, empirically calibrated alternative to ensemble architectures for continental-scale biomass mapping.
Abstract:Wall-to-wall biomass density estimation from NASA's GEDI mission requires interpolating sparse LIDAR observations across heterogeneous landscapes. While Random Forest and XGBoost are widely used, they predict each location independently from satellite features, without adapting to the varying difficulty of heterogeneous landscapes. We introduce Attentive Neural Processes (ANPs), a probabilistic meta-learning architecture that explicitly conditions predictions on local observation sets as well as geospatial foundation model embeddings. ANPs learn a flexible spatial covariance approximator that is more uncertain in complex landscapes and less in homogeneous areas. We validate this approach across five distinct biomes ranging from tropical Amazonian forests to boreal, temperate, and alpine ecosystems, demonstrating that ANPs achieve competitive accuracy while maintaining uncertainty calibration. We demonstrate a scalable, empirically calibrated alternative to ensemble architectures for continental-scale biomass mapping.
Machine Learning (cs.LG); Computational Engineering, Finance, and Science (cs.CE); Computer Vision and Pattern Recognition (cs.CV)
arXiv:2601.16834 [cs.LG]
(or arXiv:2601.16834v4 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2601.16834 Focus to learn more arXiv-issued DOI via DataCite
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