# Recent Advances in Domain Adaptation for the Classification of Remote Sensing Data arXiv: https://arxiv.org/abs/2104.07778 ยท HTML: https://mlsanity.com/paper/2104.07778/ ## Key facts - Cited by 70 papers in the map (data through 14 Oct 2026). - Builds on 1 of its 79 references that are in the map. - First posted on arXiv 15 Apr 2021. ## Builds on (most cited first, 1 of 1) - [Kernel Manifold Alignment](https://mlsanity.com/paper/1504.02338/) (2015) ## Built on by (most cited first, 30 of 70) - [Mission Critical -- Satellite Data is a Distinct Modality in Machine Learning](https://mlsanity.com/paper/2402.01444/) (2024) - [Deep Learning for Time Series Classification and Extrinsic Regression: A Current Survey](https://mlsanity.com/paper/2302.02515/) (2023) - [Enabling Country-Scale Land Cover Mapping with Meter-Resolution Satellite Imagery](https://mlsanity.com/paper/2209.00727/) (2022) - [Deep Transfer Learning for Automatic Speech Recognition: Towards Better Generalization](https://mlsanity.com/paper/2304.14535/) (2023) - [Climate-Invariant Machine Learning](https://mlsanity.com/paper/2112.08440/) (2021) - [Self-supervised remote sensing feature learning: Learning Paradigms, Challenges, and Future Works](https://mlsanity.com/paper/2211.08129/) (2022) - [APPLeNet: Visual Attention Parameterized Prompt Learning for Few-Shot Remote Sensing Image Generalization using CLIP](https://mlsanity.com/paper/2304.05995/) (2023) - [AI Security for Geoscience and Remote Sensing: Challenges and Future Trends](https://mlsanity.com/paper/2212.09360/) (2022) - [TimeMatch: Unsupervised Cross-Region Adaptation by Temporal Shift Estimation](https://mlsanity.com/paper/2111.02682/) (2021) - [Generalized Classification of Satellite Image Time Series with Thermal Positional Encoding](https://mlsanity.com/paper/2203.09175/) (2022) - [Deep Learning for Satellite Image Time Series Analysis: A Review](https://mlsanity.com/paper/2404.03936/) (2024) - [EarthShift: a benchmark for measuring robustness to real-world distribution shifts in Earth observation](https://mlsanity.com/paper/2605.29330/) (2026) - [A diverse large-scale building dataset and a novel plug-and-play domain generalization method for building extraction](https://mlsanity.com/paper/2208.10004/) (2022) - [Mapping smallholder cashew plantations to inform sustainable tree crop expansion in Benin](https://mlsanity.com/paper/2301.00363/) (2023) - [Evaluation Challenges for Geospatial ML](https://mlsanity.com/paper/2303.18087/) (2023) - [Appearance Based Deep Domain Adaptation for the Classification of Aerial Images](https://mlsanity.com/paper/2108.07779/) (2021) - [A crowdsourced dataset of aerial images with annotated solar photovoltaic arrays and installation metadata](https://mlsanity.com/paper/2209.03726/) (2022) - [Randomized Histogram Matching: A Simple Augmentation for Unsupervised Domain Adaptation in Overhead Imagery](https://mlsanity.com/paper/2104.14032/) (2021) - [Transfer Learning with Pretrained Remote Sensing Transformers](https://mlsanity.com/paper/2209.14969/) (2022) - [C-SAW: Self-Supervised Prompt Learning for Image Generalization in Remote Sensing](https://mlsanity.com/paper/2311.15812/) (2023) - [Deploying Geospatial Foundation Models in the Real World: Lessons from WorldCereal](https://mlsanity.com/paper/2508.00858/) (2025) - [Automated Extraction of Energy Systems Information from Remotely Sensed Data: A Review and Analysis](https://mlsanity.com/paper/2202.12939/) (2022) - [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) - [DDF: A Novel Dual-Domain Image Fusion Strategy for Remote Sensing Image Semantic Segmentation with Unsupervised Domain Adaptation](https://mlsanity.com/paper/2403.02784/) (2024) - [Responsible AI for Earth Observation](https://mlsanity.com/paper/2405.20868/) (2024) - [Space-scale Exploration of the Poor Reliability of Deep Learning Models: the Case of the Remote Sensing of Rooftop Photovoltaic Systems](https://mlsanity.com/paper/2408.07828/) (2024) - [Robust Building Damage Detection in Cross-Disaster Settings Using Domain Adaptation](https://mlsanity.com/paper/2603.14694/) (2026) - [Unsupervised Domain Adaptation for Semantic Segmentation using One-shot Image-to-Image Translation via Latent Representation Mixing](https://mlsanity.com/paper/2212.03826/) (2022) - [FlowEO: Generative Unsupervised Domain Adaptation for Earth Observation](https://mlsanity.com/paper/2512.05140/) (2025) - [Earth Embeddings Reveal Diverse Urban Signals from Space](https://mlsanity.com/paper/2604.03456/) (2026) ## Abstract (arXiv) The success of supervised classification of remotely sensed images acquired over large geographical areas or at short time intervals strongly depends on the representativity of the samples used to train the classification algorithm and to define the model. When training samples are collected from an image (or a spatial region) different from the one used for mapping, spectral shifts between the two distributions are likely to make the model fail. Such shifts are generally due to differences in acquisition and atmospheric conditions or to changes in the nature of the object observed. In order to design classification methods that are robust to data-set shifts, recent remote sensing literature has considered solutions based on domain adaptation (DA) approaches. Inspired by machine learning literature, several DA methods have been proposed to solve specific problems in remote sensing data classification. This paper provides a critical review of the recent advances in DA for remote sensing and presents an overview of methods divided into four categories: i) invariant feature selection; ii) representation matching; iii) adaptation of classifiers and iv) selective sampling. We provide an overview of recent methodologies, as well as examples of application of the considered techniques to real remote sensing images characterized by very high spatial and spectral resolution. Finally, we propose guidelines to the selection of the method to use in real application scenarios.