SIMPLER: Efficient Foundation Model Adaptation via Similarity-Guided Layer Pruning for Earth Observation

Fine-tuning foundation models for Earth Observation is com-putationally expensive, with high training time and memory demands forboth training and deployment. Parameter-efficient methods reduce train-ing cost but retain full inference complexity, while post-hoc compressionoptimizes inference only after costly full fine-tuning. We introduce SIM-PLER, a pre–fine-tuning architecture selection method that reduces in-ference and deployment costs by identifying an effective model depthbefore adaptation. SIMPLER exploits stabilization of representations indeeper layers of pre-trained vision transformers: it computes layer-wiserepresentation similarity on unlabeled task data and applies an auto-mated scoring function to select redundant layers, with no gradients,magnitude heuristics, or hyperparameter tuning required. On Prithvi-EO-2, SIMPLER prunes up to 79% of parameters while retaining 94% ofbaseline performance, yielding a 2.1× training speedup and 2.6× infer-ence speedup. The method generalizes to TerraMind (a multimodal EOfoundation model) and ImageNet-pretrained ViT-MAE, demonstratingapplicability across tasks, architectures, and spectral modalities. Code isavailable at https://gitlab.citius.gal/hpc4rs/simpler.