# In-Domain Self-Supervised Learning Improves Remote Sensing Image Scene Classification

arXiv: https://arxiv.org/abs/2307.01645 · HTML: https://mlsanity.com/paper/2307.01645/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 17 references that are in the map.
- First posted on arXiv 4 Jul 2023.

## Builds on (most cited first, 13 of 13)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [Emerging Properties in Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.14294/) (2021)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [iBOT: Image BERT Pre-Training with Online Tokenizer](https://mlsanity.com/paper/2111.07832/) (2021)
- [On the Variance of the Adaptive Learning Rate and Beyond](https://mlsanity.com/paper/1908.03265/) (2019)
- [Seasonal Contrast: Unsupervised Pre-Training from Uncurated Remote Sensing Data](https://mlsanity.com/paper/2103.16607/) (2021)
- [SatlasPretrain: A Large-Scale Dataset for Remote Sensing Image Understanding](https://mlsanity.com/paper/2211.15660/) (2022)
- [Advancing Plain Vision Transformer Towards Remote Sensing Foundation Model](https://mlsanity.com/paper/2208.03987/) (2022)
- [Self-supervised Learning in Remote Sensing: A Review](https://mlsanity.com/paper/2206.13188/) (2022)
- [SSL4EO-S12: A Large-Scale Multi-Modal, Multi-Temporal Dataset for Self-Supervised Learning in Earth Observation](https://mlsanity.com/paper/2211.07044/) (2022)
- [Current Trends in Deep Learning for Earth Observation: An Open-source Benchmark Arena for Image Classification](https://mlsanity.com/paper/2207.07189/) (2022)
- [AiTLAS: Artificial Intelligence Toolbox for Earth Observation](https://mlsanity.com/paper/2201.08789/) (2022)

## Built on by (most cited first, 4 of 4)

- [Geospatial foundation models for image analysis: evaluating and enhancing NASA-IBM Prithvi's domain adaptability](https://mlsanity.com/paper/2409.00489/) (2024)
- [Semantic Segmentation of Unmanned Aerial Vehicle Remote Sensing Images using SegFormer](https://mlsanity.com/paper/2410.01092/) (2024)
- [SSL4SAR: Self-Supervised Learning for Glacier Calving Front Extraction from SAR Imagery](https://mlsanity.com/paper/2507.01747/) (2025)
- [Few-Shot Remote Sensing Image Scene Classification with CLIP and Prompt Learning](https://mlsanity.com/paper/2510.24321/) (2025)

## Abstract (arXiv)

We investigate the utility of in-domain self-supervised pre-training of vision models in the analysis of remote sensing imagery. Self-supervised learning (SSL) has emerged as a promising approach for remote sensing image classification due to its ability to exploit large amounts of unlabeled data. Unlike traditional supervised learning, SSL aims to learn representations of data without the need for explicit labels. This is achieved by formulating auxiliary tasks that can be used for pre-training models before fine-tuning them on a given downstream task. A common approach in practice to SSL pre-training is utilizing standard pre-training datasets, such as ImageNet. While relevant, such a general approach can have a sub-optimal influence on the downstream performance of models, especially on tasks from challenging domains such as remote sensing. In this paper, we analyze the effectiveness of SSL pre-training by employing the iBOT framework coupled with Vision transformers trained on Million-AID, a large and unlabeled remote sensing dataset. We present a comprehensive study of different self-supervised pre-training strategies and evaluate their effect across 14 downstream datasets with diverse properties. Our results demonstrate that leveraging large in-domain datasets for self-supervised pre-training consistently leads to improved predictive downstream performance, compared to the standard approaches found in practice.