activity
20242026
collaborators

5 papers

cs.CV2026

Uncertainty-aware tree height change regression

Max Gaber, Dimitri Gominski, Jaime C. Revenga +3

Monitoring canopy height change is essential for understanding carbon sinks and forest dynamics. Remote sensing enables consistent, large-scale observations of such changes, increa…

cs.CV2026

Counting Trees from Satellite Imagery with Noisy Supervision

Dimitri Gominski, Maurice Mugabowindekwe, Qiue Xu +6

Counting individual trees is a fundamental task for environmental monitoring, yet remains largely unexplored with satellite imagery. At these resolutions, isolated trees may still…

cs.CV2025

Trees as Gaussians: Large-Scale Individual Tree Mapping

Dimitri Gominski, Martin Brandt, Xiaoye Tong +6

Trees are key components of the terrestrial biosphere, playing vital roles in ecosystem function, climate regulation, and the bioeconomy. However, large-scale monitoring of individ…

cs.CV2024

Mining Field Data for Tree Species Recognition at Scale

Dimitri Gominski, Daniel Ortiz-Gonzalo, Martin Brandt +2

Individual tree species labels are particularly hard to acquire due to the expert knowledge needed and the limitations of photointerpretation. Here, we present a methodology to aut…

cs.CV2024

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

Sizhuo Li, Dimitri Gominski, Martin Brandt +2

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 regressi…