# Extending global-local view alignment for self-supervised learning with remote sensing imagery

arXiv: https://arxiv.org/abs/2303.06670 · HTML: https://mlsanity.com/paper/2303.06670/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Builds on 41 of its 55 references that are in the map.
- First posted on arXiv 12 Mar 2023.

## Builds on (most cited first, 30 of 41)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [Emerging Properties in Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.14294/) (2021)
- [Bootstrap your own latent: A new approach to self-supervised Learning](https://mlsanity.com/paper/2006.07733/) (2020)
- [Wide Residual Networks](https://mlsanity.com/paper/1605.07146/) (2016)
- [Unsupervised Learning of Visual Features by Contrasting Cluster Assignments](https://mlsanity.com/paper/2006.09882/) (2020)
- [Improved Baselines with Momentum Contrastive Learning](https://mlsanity.com/paper/2003.04297/) (2020)
- [Unsupervised Feature Learning via Non-Parametric Instance-level Discrimination](https://mlsanity.com/paper/1805.01978/) (2018)
- [An Empirical Study of Training Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.02057/) (2021)
- [EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification](https://mlsanity.com/paper/1709.00029/) (2017)
- [Context Encoders: Feature Learning by Inpainting](https://mlsanity.com/paper/1604.07379/) (2016)
- [Big Self-Supervised Models are Strong Semi-Supervised Learners](https://mlsanity.com/paper/2006.10029/) (2020)
- [Colorful Image Colorization](https://mlsanity.com/paper/1603.08511/) (2016)
- [Unsupervised Visual Representation Learning by Context Prediction](https://mlsanity.com/paper/1505.05192/) (2015)
- [Deep Clustering for Unsupervised Learning of Visual Features](https://mlsanity.com/paper/1807.05520/) (2018)
- [What Makes for Good Views for Contrastive Learning?](https://mlsanity.com/paper/2005.10243/) (2020)
- [Self-Supervised Learning of Pretext-Invariant Representations](https://mlsanity.com/paper/1912.01991/) (2019)
- [Discriminative Unsupervised Feature Learning with Exemplar Convolutional Neural Networks](https://mlsanity.com/paper/1406.6909/) (2014)
- [BigEarthNet: A Large-Scale Benchmark Archive For Remote Sensing Image Understanding](https://mlsanity.com/paper/1902.06148/) (2019)
- [Geography-Aware Self-Supervised Learning](https://mlsanity.com/paper/2011.09980/) (2020)
- [Self-supervised Learning in Remote Sensing: A Review](https://mlsanity.com/paper/2206.13188/) (2022)
- [SEED: Self-supervised Distillation For Visual Representation](https://mlsanity.com/paper/2101.04731/) (2021)
- [Urban Change Detection for Multispectral Earth Observation Using Convolutional Neural Networks](https://mlsanity.com/paper/1810.08468/) (2018)

## Built on by (most cited first, 20 of 20)

- [CROMA: Remote Sensing Representations with Contrastive Radar-Optical Masked Autoencoders](https://mlsanity.com/paper/2311.00566/) (2023)
- [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)
- [A Survey on Remote Sensing Foundation Models: From Vision to Multimodality](https://mlsanity.com/paper/2503.22081/) (2025)
- [Foundation Models in Remote Sensing: Evolving from Unimodality to Multimodality](https://mlsanity.com/paper/2603.00988/) (2026)
- [SeaMo: A Season-Aware Multimodal Foundation Model for Remote Sensing](https://mlsanity.com/paper/2412.19237/) (2024)
- [Parameter-Efficient Adaptation of Geospatial Foundation Models through Embedding Deflection](https://mlsanity.com/paper/2503.09493/) (2025)
- [CromSS: Cross-modal pre-training with noisy labels for remote sensing image segmentation](https://mlsanity.com/paper/2405.01217/) (2024)
- [Agentic AI in Remote Sensing: Foundations, Taxonomy, and Emerging Systems](https://mlsanity.com/paper/2601.01891/) (2026)
- [TESSERA v2: Scaling Pixel-wise Earth Foundation Models](https://mlsanity.com/paper/2607.03949/) (2026)
- [Enhancing Compositional Generalization via Compositional Feature Alignment](https://mlsanity.com/paper/2402.02851/) (2024)
- [GeoCrossBench: Cross-Band Generalization for Remote Sensing](https://mlsanity.com/paper/2511.02831/) (2025)
- [DeepAndes: A Self-Supervised Vision Foundation Model for Multi-Spectral Remote Sensing Imagery of the Andes](https://mlsanity.com/paper/2504.20303/) (2025)
- [SSL4SAR: Self-Supervised Learning for Glacier Calving Front Extraction from SAR Imagery](https://mlsanity.com/paper/2507.01747/) (2025)
- [MAESTRO: Masked AutoEncoders for Multimodal, Multitemporal, and Multispectral Earth Observation Data](https://mlsanity.com/paper/2508.10894/) (2025)
- [Brewing Stronger Features: Dual-Teacher Distillation for Multispectral Earth Observation](https://mlsanity.com/paper/2602.19863/) (2026)
- [SIMPLER: Efficient Foundation Model Adaptation via Similarity-Guided Layer Pruning for Earth Observation](https://mlsanity.com/paper/2603.19873/) (2026)
- [Scalable Label-efficient Footpath Network Generation Using Remote Sensing Data and Self-supervised Learning](https://mlsanity.com/paper/2309.09446/) (2023)
- [SatDINO: A Deep Dive into Self-Supervised Pretraining for Remote Sensing](https://mlsanity.com/paper/2508.21402/) (2025)
- [Environment-Aware Satellite Image Generation with Diffusion Models](https://mlsanity.com/paper/2509.24875/) (2025)

## Abstract (arXiv)

Since large number of high-quality remote sensing images are readily accessible, exploiting the corpus of images with less manual annotation draws increasing attention. Self-supervised models acquire general feature representations by formulating a pretext task that generates pseudo-labels for massive unlabeled data to provide supervision for training. While prior studies have explored multiple self-supervised learning techniques in remote sensing domain, pretext tasks based on local-global view alignment remain underexplored, despite achieving state-of-the-art results on natural imagery. Inspired by DINO, which employs an effective representation learning structure with knowledge distillation based on global-local view alignment, we formulate two pretext tasks for self-supervised learning on remote sensing imagery (SSLRS). Using these tasks, we explore the effectiveness of positive temporal contrast as well as multi-sized views on SSLRS. We extend DINO and propose DINO-MC which uses local views of various sized crops instead of a single fixed size in order to alleviate the limited variation in object size observed in remote sensing imagery. Our experiments demonstrate that even when pre-trained on only 10% of the dataset, DINO-MC performs on par or better than existing state-of-the-art SSLRS methods on multiple remote sensing tasks, while using less computational resources. All codes, models, and results are released at https://github.com/WennyXY/DINO-MC.