Martin Brandt — accepted AI-conference papers

2024

Estimating Canopy Height at Scale

Jan Pauls, Max Zimmer, Una M. Kelly, Martin Schwartz, Sassan Saatchi, Philippe CIAIS +3

ICML 2024poster

We propose a framework for global-scale canopy height estimation based on satellite data. Our model leverages advanced data preprocessing techniques, resorts to a novel loss function designed to counter geolocation inaccuracies inherent in the ground-truth height measurements, and employs data from…

Cited by 6 Source ↗PDF ↗code ↗Scholar ↗

2024

Get Your Embedding Space in Order: Domain-Adaptive Regression for Forest Monitoring

Sizhuo Li, Dimitri Gominski*, Martin Brandt, Xiaoye Tong, Philippe Ciais

ECCV 2024poster

"Image-level regression is an important task in Earth observation, where visual domain and label shifts are a core challenge hampering generalization. However, cross-domain regression within remote sensing data remains understudied due to the absence of suited datasets. We introduce a new dataset wi…

Cited by 0 Source ↗PDF ↗code ↗Scholar ↗