most citedTESSERA v2: Scaling Pixel-wise Earth Foundation Models

6 citations · 6 across the 2 of their papers we have counts for

collaborators

9 papers

cs.HC2026

How Usable Are Geospatial Foundation Models? A Systematic Evaluation of 89 Models

Robin Young, Artyom Gabtraupov, Kenzy Soror +1

Geospatial foundation models (GeoFMs) offer transformative potential for environmental monitoring, yet adoption among ecologists is uneven. Most evaluations are model-centric, focu…

cs.CV20266 cited

TESSERA v2: Scaling Pixel-wise Earth Foundation Models

Zhengpeng Feng, Sadiq Jaffer, Ira Shokar +13

Pixel-wise Earth-observation (EO) foundation models are now achieving state-of-the-art performance via generated spatial embeddings. However, how these models scale and how best to…

cs.LG2026

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.…

cs.LG2026

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…

cs.LG2026

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…

cs.LG2026

Interpolation of GEDI Biomass Estimates with Calibrated Uncertainty Quantification

Robin Young, Srinivasan Keshav

Reliable wall-to-wall biomass density estimation from NASA's GEDI mission requires interpolating sparse LIDAR observations across heterogeneous landscapes. While machine learning a…