Linus Mathias Scheibenreif
Last Name
Scheibenreif
First name
Linus Mathias
Email
linus.scheibenreif@unisg.ch
Phone
+41 71 224 26 98
1 results
Now showing 1 - 1 of 1
- Some of the metrics are blocked by yourconsent settings
Item type:Publication, MAPEX: Modality-Aware Pruning of Experts for Remote Sensing Foundation Models(2026-01-15); ; Remote sensing data is commonly used in a wide range of tasks, such as natural disaster monitoring and land-use studies. For each task, scientists carefully choose appropriate modalities or leverage data from purpose-built instruments. Recent work on remote sensing foundation models pre-trains computer vision models on large amounts of remote sensing data to learn general-purpose representations. However, this progress comes at the cost of very large models, which are particularly challenging to deploy on edge devices due to their high inference costs. Moreover, downstream applications often rely on only a subset of modalities and operate under strict resource constraints, creating a mismatch between the large, multi-purpose models produced by pre-training and the lightweight, task-specific models needed in practice. We address this mismatch with MAPEX, a remote sensing foundation model based on mixture-of-modality experts. MAPEX is pre-trained on multi-modal remote sensing data using a novel modality-conditioned token routing mechanism that naturally encourages the emergence of modality-specialized experts. To apply the model on a specific task, we propose a modality-aware pruning technique that retains only the experts relevant to the task’s modalities, resulting in lightweight, task-specific models that can be directly extracted from the pre-trained foundation model, at no additional cost. Our approach yields efficient modality-specific models while simplifying fine-tuning and deployment for the modalities of interest. We experimentally validate MAPEX on diverse remote sensing datasets and show strong performance compared to fully supervised training and state-of-the-art remote sensing foundation models. Code will be available at https://github.com/HSG-AIML/MAPEX}{github.com/HSG-AIML/MAPEX.Type:journal articleVolume:64