12 citations · 12 across the 3 of their papers we have counts for
8 papers
GeoSANE: Learning Geospatial Representations from Models, Not Data
Joelle Hanna, Damian Falk, Stella X. Yu +1
Recent advances in remote sensing have led to an increase in the number of available foundation models; each trained on different modalities, datasets, and objectives, yet capturin…
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…
Know Your Attention Maps: Class-specific Token Masking for Weakly Supervised Semantic Segmentation
Joelle Hanna, Damian Borth
Weakly Supervised Semantic Segmentation (WSSS) is a challenging problem that has been extensively studied in recent years. Traditional approaches often rely on external modules lik…
SAR-to-RGB Translation with Latent Diffusion for Earth Observation
Kaan Aydin, Joelle Hanna, Damian Borth
Earth observation satellites like Sentinel-1 (S1) and Sentinel-2 (S2) provide complementary remote sensing (RS) data, but S2 images are often unavailable due to cloud cover or data…
Neural Plasticity-Inspired Multimodal Foundation Model for Earth Observation
Zhitong Xiong, Yi Wang, Fahong Zhang +7
Earth observation (EO) in open-world settings presents a unique challenge: different applications rely on diverse sensor modalities, each with varying ground sampling distances, sp…
Ben-ge: Extending BigEarthNet with Geographical and Environmental Data
Michael Mommert, Nicolas Kesseli, Joëlle Hanna +3
Deep learning methods have proven to be a powerful tool in the analysis of large amounts of complex Earth observation data. However, while Earth observation data are multi-modal in…