collaborators

5 papers

cs.CV2026

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

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…

cs.LG2025

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…

cs.CV2025

Scaling Up Forest Vision with Synthetic Data

Yihang She, Andrew Blake, David Coomes +1

Accurate tree segmentation is a key step in extracting individual tree metrics from forest laser scans, and is essential to understanding ecosystem functions in carbon cycling and…