8 papers
Above-ground Biomass Estimation with Geospatial Foundation Models
Ghjulia Sialellia, Linus Scheibenreif, Jan Dirk Wegner +1
Accurate estimation of Above-Ground Biomass (AGB) from satellite imagery is essential for the large-scale monitoring of carbon stocks, yet it remains a challenging regression task…
From Machine Learning to Large-Scale EO Products: Best Practices for Making Maps
Ghjulia Sialelli, Robin Young, Yuchang Jiang +9
Recent years have seen a rapid expansion in the production of large-scale geospatial maps derived from Earth observation (EO) data, driven largely by advances in machine learning (…
Reconstructing Multi-Decadal Forest Disturbances: A Spatio-Temporal Transformer Approach
Linus Scheibenreif, Anton Raichuk, Maxim Neumann
Accurate monitoring of forest disturbances is essential for understanding carbon dynamics and land management, yet traditional approaches typically rely on pixel-wise analysis of s…
The Potential of Copernicus Satellites for Disaster Response: Retrieving Building Damage from Sentinel-1 and Sentinel-2
Olivier Dietrich, Merlin Alfredsson, Emilia Arens +5
Natural disasters demand rapid damage assessment to guide humanitarian response. Here, we investigate whether medium-resolution Earth observation images from the Copernicus program…
MAPEX: Modality-Aware Pruning of Experts for Remote Sensing Foundation Models
Joelle Hanna, Linus Scheibenreif, Damian Borth
Remote sensing data is commonly used for tasks such as flood mapping, wildfire detection, or land-use studies. For each task, scientists carefully choose appropriate modalities or…
Fine-tune Smarter, Not Harder: Parameter-Efficient Fine-Tuning for Geospatial Foundation Models
Francesc Marti-Escofet, Benedikt Blumenstiel, Linus Scheibenreif +2
Earth observation (EO) is crucial for monitoring environmental changes, responding to disasters, and managing natural resources. In this context, foundation models facilitate remot…