# SenPa-MAE: Sensor Parameter Aware Masked Autoencoder for Multi-Satellite Self-Supervised Pretraining

arXiv: https://arxiv.org/abs/2408.11000 · HTML: https://mlsanity.com/paper/2408.11000/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Aug 2024.

## Built on by (most cited first, 18 of 18)

- [Towards a Unified Copernicus Foundation Model for Earth Vision](https://mlsanity.com/paper/2503.11849/) (2025)
- [TESSERA: Temporal Embeddings of Surface Spectra for Earth Representation and Analysis](https://mlsanity.com/paper/2506.20380/) (2025)
- [Panopticon: Advancing Any-Sensor Foundation Models for Earth Observation](https://mlsanity.com/paper/2503.10845/) (2025)
- [FlexiMo: A Flexible Remote Sensing Foundation Model](https://mlsanity.com/paper/2503.23844/) (2025)
- [Foundation Models in Remote Sensing: Evolving from Unimodality to Multimodality](https://mlsanity.com/paper/2603.00988/) (2026)
- [SMARTIES: Spectrum-Aware Multi-Sensor Auto-Encoder for Remote Sensing Images](https://mlsanity.com/paper/2506.19585/) (2025)
- [TiMo: Spatiotemporal Foundation Model for Satellite Image Time Series](https://mlsanity.com/paper/2505.08723/) (2025)
- [UniverSat: Resolution- and Modality-Agnostic Transformers for Earth Observation](https://mlsanity.com/paper/2606.23503/) (2026)
- [MAESTRO: Masked AutoEncoders for Multimodal, Multitemporal, and Multispectral Earth Observation Data](https://mlsanity.com/paper/2508.10894/) (2025)
- [Location Is All You Need: Continuous Spatiotemporal Neural Representations of Earth Observation Data](https://mlsanity.com/paper/2604.07092/) (2026)
- [UNIGEOCLIP: Unified Geospatial Contrastive Learning](https://mlsanity.com/paper/2604.11668/) (2026)
- [MIMRS: A Survey on Masked Image Modeling in Remote Sensing](https://mlsanity.com/paper/2504.03181/) (2025)
- [SARFormer -- An Acquisition Parameter Aware Vision Transformer for Synthetic Aperture Radar Data](https://mlsanity.com/paper/2504.08441/) (2025)
- [Atomizer: Generalizing to new modalities by breaking satellite images down to a set of scalars](https://mlsanity.com/paper/2506.13542/) (2025)
- [Local Precise Refinement: A Dual-Gated Mixture-of-Experts for Enhancing Foundation Model Generalization against Spectral Shifts](https://mlsanity.com/paper/2603.13352/) (2026)
- [Emerging Flexible Designs for Geospatial Multimodal Foundation Models](https://mlsanity.com/paper/2606.12595/) (2026)
- [Beyond Accuracy: Assessing Calibration of Geospatial Foundation Models and Their Sensitivity to Distribution Shifts](https://mlsanity.com/paper/2608.16614/) (2026)
- [Atomizer-IO: Beyond Pixels, Patches and Grids](https://mlsanity.com/paper/2609.40320/) (2026)

## Abstract (arXiv)

This paper introduces SenPa-MAE, a transformer architecture that encodes the sensor parameters of an observed multispectral signal into the image embeddings. SenPa-MAE can be pre-trained on imagery of different satellites with non-matching spectral or geometrical sensor characteristics. To incorporate sensor parameters, we propose a versatile sensor parameter encoding module as well as a data augmentation strategy for the diversification of the pre-training dataset. This enables the model to effectively differentiate between various sensors and gain an understanding of sensor parameters and the correlation to the observed signal. Given the rising number of Earth observation satellite missions and the diversity in their sensor specifications, our approach paves the way towards a sensor-independent Earth observation foundation model. This opens up possibilities such as cross-sensor training and sensor-independent inference.