OPTIMUS: Observing Persistent Transformations in Multi-temporal Unlabeled Satellite-data

In the face of pressing environmental issues in the 21st century, monitoring surface changes on Earth is more important than ever. Large-scale remote sensing, such as satellite imagery, is an important tool for this task. However, using supervised methods to detect changes is difficult because of the lack of satellite data annotated with change labels, especially for rare categories of change. Annotation proves challenging due to the sparse occurrence of changes in satellite images. Even within a vast collection of images, only a small fraction may exhibit persistent changes of interest. To address this challenge, we introduce OPTIMUS, a self-supervised learning method based on an intuitive principle: if a model can recover information about the relative order of images in the time series, then that implies that there are long-lasting changes in the images. OPTIMUS demonstrates this principle by using change point detection methods on model outputs in a time series. We demonstrate that OPTIMUS can directly detect interesting changes in satellite images, achieving an improvement in AUROC score from 56.3% to 87.6% at distinguishing changed time series from unchanged ones compared to baselines. Our code and dataset are available atthis https URL.

@article{yu2025_2506.13902,
  title={ OPTIMUS: Observing Persistent Transformations in Multi-temporal Unlabeled Satellite-data },
  author={ Raymond Yu and Paul Han and Josh Myers-Dean and Piper Wolters and Favyen Bastani },
  journal={arXiv preprint arXiv:2506.13902},
  year={ 2025 }
}