40 citations · 49 across the 10 of their papers we have counts for
8 papers · 1 filter
Geospatial embeddings detect old-growth forests but buffered spatial validation narrows their advantage over Sentinel features
Thomas Ratsakatika, Mihai Zotta, Srinivasan Keshav +1
Old-growth forests develop over centuries under minimal anthropogenic disturbance, producing structurally complex and biodiverse stands. In Europe, protecting them requires mapping…
Neural Processes Maintain Calibrated Biomass Estimates Across Spatiotemporal Gaps and Disturbance
Robin Young, Srinivasan Keshav
Monitoring deforestation-driven carbon emissions requires both spatially explicit and temporally continuous estimates of aboveground biomass density (AGBD) with calibrated uncertai…
Below-ground Fungal Biodiversity Can be Monitored Using Self-Supervised Learning Satellite Features
Robin Young, Michael E. Van Nuland, E. Toby Kiers +4
Mycorrhizal fungi are vital to terrestrial ecosystem functioning. Yet monitoring their biodiversity at landscape scales is often unfeasible due to time and cost constraints. Curren…
Interpolation of GEDI Biomass Estimates with Calibrated Uncertainty Quantification
Robin Young, Srinivasan Keshav
Wall-to-wall biomass density estimation from NASA's GEDI mission requires interpolating sparse LIDAR observations across heterogeneous landscapes. While Random Forest and XGBoost a…
Embedding -based Crop Type Classification in the Groundnut Basin of Senegal
Madeline C. Lisaius, Srinivasan Keshav, Andrew Blake +1
Crop type maps from satellite remote sensing are important tools for food security, local livelihood support and climate change mitigation in smallholder regions of the world, but…
TESSERA: Temporal Embeddings of Surface Spectra for Earth Representation and Analysis
Zhengpeng Feng, Clement Atzberger, Sadiq Jaffer +11
Satellite Earth-observation (EO) time series in the optical and microwave ranges of the electromagnetic spectrum are often irregular due to orbital patterns and cloud obstruction.…
PILA: Physics-Informed Low Rank Augmentation for Interpretable Earth Observation
Yihang She, Andrew Blake, Clement Atzberger +2
Physically meaningful representations are essential for Earth Observation (EO), yet existing physical models are often simplified and incomplete. This leads to discrepancies betwee…
From Spectra to Biophysical Insights: End-to-End Learning with a Biased Radiative Transfer Model
Yihang She, Clement Atzberger, Andrew Blake +1
Advances in machine learning have boosted the use of Earth observation data for climate change research. Yet, the interpretability of machine-learned representations remains a chal…