1 citations · 2 across the 2 of their papers we have counts for
4 papers
PiCo: Physics-Informed Concept Probing for Geo-Foundation Models
Yihang She, Andrew Blake, Jianbo Qi +1
Geo-foundation models (GFMs) are typically evaluated by downstream task predictive performance, revealing little about which physical features they are sensitive to or use in predi…
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…
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…