40 citations · 49 across the 6 of their papers we have counts for
7 papers
Annual Earth-observation embeddings encode wildfire disturbance and support simplified burned area mapping
Jovana Knezevic, Clement Atzberger, Zhengpeng Feng +3
Medium-resolution (10-30 m) burned area mapping is vital for monitoring wildfires and their impacts, but remains difficult to scale. Existing methods require either curated fire-sp…
TESSERA v2: Scaling Pixel-wise Earth Foundation Models
Zhengpeng Feng, Sadiq Jaffer, Ira Shokar +12
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…
Exploring the potential of AlphaEarth and TESSERA embeddings for Fine-scale Local Climate Zone Mapping: A case study across five cities in Switzerland
Htet Yamin Ko Ko, Clement Atzberger
Understanding urban spatial morphology is critical for climate modeling, risk assessment, and sustainable urban design, and Local Climate Zone (LCZ) mapping provides the basic fram…
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…