7 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 (…
LitePT: Lighter Yet Stronger Point Transformer
Yuanwen Yue, Damien Robert, Jianyuan Wang +4
Modern neural architectures for 3D point cloud processing contain both convolutional layers and attention blocks, but the best way to assemble them remains unclear. We analyse the…
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…
Tree crop mapping of South America reveals links to deforestation and conservation
Yuchang Jiang, Anton Raichuk, Xiaoye Tong +6
Monitoring tree crop expansion is vital for zero-deforestation policies like the European Union's Regulation on Deforestation-free Products (EUDR). However, these efforts are hinde…
Thera: Aliasing-Free Arbitrary-Scale Super-Resolution with Neural Heat Fields
Alexander Becker, Rodrigo Caye Daudt, Dominik Narnhofer +4
Recent approaches to arbitrary-scale single image super-resolution (ASR) use neural fields to represent continuous signals that can be sampled at arbitrary resolutions. However, po…