most citedTESSERA v2: Scaling Pixel-wise Earth Foundation Models

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

collaborators

6 papers

cs.LG2026

Earth observation embeddings are effective sub-grid descriptors for probabilistic weather downscaling

Pedro Sousa, Will Tebbutt, Sadiq Jaffer +3

Global weather reanalyses and forecasts resolve the evolving atmospheric state on coarse grids, but site-specific applications require predictions at arbitrary locations where near…

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

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…

cs.AI2026

Information-theoretic Distinctions Between Deception and Confusion

Robin Young

We propose an information-theoretic formalization of the distinction between two fundamental AI safety failure modes: deceptive alignment and goal drift. While both can lead to sys…

astro-ph.EP2025

Systematic determination of dust properties for a sample of 133 spatially resolved debris discs

J. P. Marshall, S. Hengst, R. Young +6

Determination of the composition and size distribution of dust grains in debris discs is strongly dependent on constraining the underlying spatial distribution of that dust through…