# Self-supervised pre-training enhances change detection in Sentinel-2 imagery

arXiv: https://arxiv.org/abs/2101.08122 · HTML: https://mlsanity.com/paper/2101.08122/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 32 references that are in the map.
- First posted on arXiv 20 Jan 2021.

## Builds on (most cited first, 10 of 10)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Unsupervised Representation Learning by Predicting Image Rotations](https://mlsanity.com/paper/1803.07728/) (2018)
- [How transferable are features in deep neural networks?](https://mlsanity.com/paper/1411.1792/) (2014)
- [Unsupervised Visual Representation Learning by Context Prediction](https://mlsanity.com/paper/1505.05192/) (2015)
- [Fully Convolutional Siamese Networks for Change Detection](https://mlsanity.com/paper/1810.08462/) (2018)
- [Revisiting Self-Supervised Visual Representation Learning](https://mlsanity.com/paper/1901.09005/) (2019)
- [Deep learning in remote sensing: a review](https://mlsanity.com/paper/1710.03959/) (2017)
- [Urban Change Detection for Multispectral Earth Observation Using Convolutional Neural Networks](https://mlsanity.com/paper/1810.08468/) (2018)
- [Learning Spectral-Spatial-Temporal Features via a Recurrent Convolutional Neural Network for Change Detection in Multispectral Imagery](https://mlsanity.com/paper/1803.02642/) (2018)
- [Guided Anisotropic Diffusion and Iterative Learning for Weakly Supervised Change Detection](https://mlsanity.com/paper/1904.08208/) (2019)

## Built on by (most cited first, 21 of 21)

- [Self-supervised Learning in Remote Sensing: A Review](https://mlsanity.com/paper/2206.13188/) (2022)
- [SSL4EO-L: Datasets and Foundation Models for Landsat Imagery](https://mlsanity.com/paper/2306.09424/) (2023)
- [EarthNets: Empowering AI in Earth Observation](https://mlsanity.com/paper/2210.04936/) (2022)
- [Change Detection Meets Visual Question Answering](https://mlsanity.com/paper/2112.06343/) (2021)
- [Self-supervised Audiovisual Representation Learning for Remote Sensing Data](https://mlsanity.com/paper/2108.00688/) (2021)
- [Self-supervised remote sensing feature learning: Learning Paradigms, Challenges, and Future Works](https://mlsanity.com/paper/2211.08129/) (2022)
- [Semantic-aware Dense Representation Learning for Remote Sensing Image Change Detection](https://mlsanity.com/paper/2205.13769/) (2022)
- [Inferring 3D change detection from bitemporal optical images](https://mlsanity.com/paper/2205.15903/) (2022)
- [Generalizable Disaster Damage Assessment via Change Detection with Vision Foundation Model](https://mlsanity.com/paper/2406.08020/) (2024)
- [Self-supervised Remote Sensing Images Change Detection at Pixel-level](https://mlsanity.com/paper/2105.08501/) (2021)
- [SMARTIES: Spectrum-Aware Multi-Sensor Auto-Encoder for Remote Sensing Images](https://mlsanity.com/paper/2506.19585/) (2025)
- [Evaluating Self and Semi-Supervised Methods for Remote Sensing Segmentation Tasks](https://mlsanity.com/paper/2111.10079/) (2021)
- [Be the Change You Want to See: Revisiting Remote Sensing Change Detection Practices](https://mlsanity.com/paper/2507.03367/) (2025)
- [Semantic decoupled representation learning for remote sensing image change detection](https://mlsanity.com/paper/2201.05778/) (2022)
- [Supervising Remote Sensing Change Detection Models with 3D Surface Semantics](https://mlsanity.com/paper/2202.13251/) (2022)
- [Self-supervised learning -- A way to minimize time and effort for precision agriculture?](https://mlsanity.com/paper/2204.02100/) (2022)
- [Deep Metric Learning for Unsupervised Remote Sensing Change Detection](https://mlsanity.com/paper/2303.09536/) (2023)
- [Unsupervised CD in satellite image time series by contrastive learning and feature tracking](https://mlsanity.com/paper/2304.11375/) (2023)
- [EuroMineNet: A Multitemporal Sentinel-2 Benchmark for Spatiotemporal Mining Footprint Analysis in the European Union (2015-2024)](https://mlsanity.com/paper/2510.14661/) (2025)
- [Contrastive pretraining for semantic segmentation is robust to noisy positive pairs](https://mlsanity.com/paper/2211.13756/) (2022)
- [Geodiffussr: Generative Terrain Texturing with Elevation Fidelity](https://mlsanity.com/paper/2511.23029/) (2025)

## Abstract (arXiv)

While annotated images for change detection using satellite imagery are scarce and costly to obtain, there is a wealth of unlabeled images being generated every day. In order to leverage these data to learn an image representation more adequate for change detection, we explore methods that exploit the temporal consistency of Sentinel-2 times series to obtain a usable self-supervised learning signal. For this, we build and make publicly available (https://zenodo.org/record/4280482) the Sentinel-2 Multitemporal Cities Pairs (S2MTCP) dataset, containing multitemporal image pairs from 1520 urban areas worldwide. We test the results of multiple self-supervised learning methods for pre-training models for change detection and apply it on a public change detection dataset made of Sentinel-2 image pairs (OSCD).