# DAugNet: Unsupervised, Multi-source, Multi-target, and Life-long Domain Adaptation for Semantic Segmentation of Satellite Images

arXiv: https://arxiv.org/abs/2005.06216 · HTML: https://mlsanity.com/paper/2005.06216/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 May 2020.

## Built on by (most cited first, 13 of 13)

- [A Survey on Continual Semantic Segmentation: Theory, Challenge, Method and Application](https://mlsanity.com/paper/2310.14277/) (2023)
- [Image Fusion in Remote Sensing: An Overview and Meta Analysis](https://mlsanity.com/paper/2401.08837/) (2024)
- [Randomized Histogram Matching: A Simple Augmentation for Unsupervised Domain Adaptation in Overhead Imagery](https://mlsanity.com/paper/2104.14032/) (2021)
- [Birds of A Feather Flock Together: Category-Divergence Guidance for Domain Adaptive Segmentation](https://mlsanity.com/paper/2204.02111/) (2022)
- [Integrating Multiple Sources Knowledge for Class Asymmetry Domain Adaptation Segmentation of Remote Sensing Images](https://mlsanity.com/paper/2305.09893/) (2023)
- [A Review of Landcover Classification with Very-High Resolution Remotely Sensed Optical Images-Analysis Unit,Model Scalability and Transferability](https://mlsanity.com/paper/2202.03342/) (2022)
- [High-resolution semantically-consistent image-to-image translation](https://mlsanity.com/paper/2209.06264/) (2022)
- [Training Domain-invariant Object Detector Faster with Feature Replay and Slow Learner](https://mlsanity.com/paper/2105.14693/) (2021)
- [Synthetic Data Matters: Re-training with Geo-typical Synthetic Labels for Building Detection](https://mlsanity.com/paper/2507.16657/) (2025)
- [Deep Learning Based Domain Adaptation Methods in Remote Sensing: A Comprehensive Survey](https://mlsanity.com/paper/2510.15615/) (2025)
- [On the Transferability of Learning Models for Semantic Segmentation for Remote Sensing Data](https://mlsanity.com/paper/2310.10490/) (2023)
- [Self-supervised Domain-agnostic Domain Adaptation for Satellite Images](https://mlsanity.com/paper/2309.11109/) (2023)
- [Hierarchical Bayesian Framework for Multisource Domain Adaptation](https://mlsanity.com/paper/2512.18553/) (2025)

## Abstract (arXiv)

The domain adaptation of satellite images has recently gained an increasing attention to overcome the limited generalization abilities of machine learning models when segmenting large-scale satellite images. Most of the existing approaches seek for adapting the model from one domain to another. However, such single-source and single-target setting prevents the methods from being scalable solutions, since nowadays multiple source and target domains having different data distributions are usually available. Besides, the continuous proliferation of satellite images necessitates the classifiers to adapt to continuously increasing data. We propose a novel approach, coined DAugNet, for unsupervised, multi-source, multi-target, and life-long domain adaptation of satellite images. It consists of a classifier and a data augmentor. The data augmentor, which is a shallow network, is able to perform style transfer between multiple satellite images in an unsupervised manner, even when new data are added over the time. In each training iteration, it provides the classifier with diversified data, which makes the classifier robust to large data distribution difference between the domains. Our extensive experiments prove that DAugNet significantly better generalizes to new geographic locations than the existing approaches.