Faster, Lighter, Better: Compressed Earth Observation Foundation Models

Earth observation foundation models (EOFMs) support diverse geospatial tasks, but adapting and deploying them could be computationally expensive. We introduce NewOrder, a framework for structured pruning of EOFMs before full downstream fine-tuning. Structural importance can change as the pretrained encoder and a newly initialized decoder learn together, while heterogeneous EO data make importance estimates sensitive to the calibration inputs. NewOrder addresses these challenges by combining output-sensitive scoring, progressive pruning, and representative calibration. Across four encoders and four tasks, NewOrder with 50% compression rate achieves the best performance among the compared pruning methods in 14 of 16 settings and exceeds dense adaptation in 8 of them. Additionally, full efficiency profiling shows up to 1.73× inference speedup, 1.72× training-step speedup, and 33.4% lower peak training memory. These results demonstrate that selecting which pretrained structures to retain during adaptation can improve both downstream accuracy and computational efficiency, making the selection of retained pretrained capacity an effective part of task adaptation