# Deep Learning for Change Detection in Remote Sensing Images: Comprehensive Review and Meta-Analysis

arXiv: https://arxiv.org/abs/2006.05612 · HTML: https://mlsanity.com/paper/2006.05612/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jun 2020.

## Built on by (most cited first, 30 of 34)

- [Self-supervised Learning in Remote Sensing: A Review](https://mlsanity.com/paper/2206.13188/) (2022)
- [A Billion-scale Foundation Model for Remote Sensing Images](https://mlsanity.com/paper/2304.05215/) (2023)
- [TINYCD: A (Not So) Deep Learning Model For Change Detection](https://mlsanity.com/paper/2207.13159/) (2022)
- [A Review on Deep Learning in UAV Remote Sensing](https://mlsanity.com/paper/2101.10861/) (2021)
- [Self-Supervised Learning for Invariant Representations from Multi-Spectral and SAR Images](https://mlsanity.com/paper/2205.02049/) (2022)
- [DDPM-CD: Denoising Diffusion Probabilistic Models as Feature Extractors for Change Detection](https://mlsanity.com/paper/2206.11892/) (2022)
- [A Mamba-based Siamese Network for Remote Sensing Change Detection](https://mlsanity.com/paper/2407.06839/) (2024)
- [MapFormer: Boosting Change Detection by Using Pre-change Information](https://mlsanity.com/paper/2303.17859/) (2023)
- [Deep Active Learning for Multi-Label Classification of Remote Sensing Images](https://mlsanity.com/paper/2212.01165/) (2022)
- [Self-supervised Lesion Change Detection and Localisation in Longitudinal Multiple Sclerosis Brain Imaging](https://mlsanity.com/paper/2106.00919/) (2021)
- [BLDNet: A Semi-supervised Change Detection Building Damage Framework using Graph Convolutional Networks and Urban Domain Knowledge](https://mlsanity.com/paper/2201.10389/) (2022)
- [Deep Metric Learning for Unsupervised Remote Sensing Change Detection](https://mlsanity.com/paper/2303.09536/) (2023)
- [ViLaCD-R1: A Vision-Language Framework for Semantic Change Detection in Remote Sensing](https://mlsanity.com/paper/2512.23244/) (2025)
- [Towards Cross-Disaster Building Damage Assessment with Graph Convolutional Networks](https://mlsanity.com/paper/2201.10395/) (2022)
- [Irregular Change Detection in Sparse Bi-Temporal Point Clouds using Learned Place Recognition Descriptors and Point-to-Voxel Comparison](https://mlsanity.com/paper/2306.15416/) (2023)
- [Weakly Supervised Change Detection via Knowledge Distillation and Multiscale Sigmoid Inference](https://mlsanity.com/paper/2403.05796/) (2024)
- [Tree semantic segmentation from aerial image time series](https://mlsanity.com/paper/2407.13102/) (2024)
- [Exploiting Digital Surface Models for Inferring Super-Resolution for Remotely Sensed Images](https://mlsanity.com/paper/2205.04056/) (2022)
- [Unsupervised Wildfire Change Detection based on Contrastive Learning](https://mlsanity.com/paper/2211.14654/) (2022)
- [Deep-Learning-based Change Detection with Spaceborne Hyperspectral PRISMA data](https://mlsanity.com/paper/2310.13627/) (2023)
- [SeFi-CD: A Semantic First Change Detection Paradigm That Can Detect Any Change You Want](https://mlsanity.com/paper/2407.09874/) (2024)
- [Lightweight Adapter Learning for More Generalized Remote Sensing Change Detection](https://mlsanity.com/paper/2504.19598/) (2025)
- [Remote Sensing Change Detection via Weak Temporal Supervision](https://mlsanity.com/paper/2601.02126/) (2026)
- [RemoteVAR: Autoregressive Visual Modeling for Remote Sensing Change Detection](https://mlsanity.com/paper/2601.11898/) (2026)
- [Weakly Supervised Learning with Automated Labels from Radiology Reports for Glioma Change Detection](https://mlsanity.com/paper/2210.09698/) (2022)
- [SA-DNet: A on-demand semantic object registration network adapting to non-rigid deformation](https://mlsanity.com/paper/2210.09900/) (2022)
- [Monitoring of Urban Changes with multi-modal Sentinel 1 and 2 Data in Mariupol, Ukraine, in 2022/23](https://mlsanity.com/paper/2309.08607/) (2023)
- [Combining recurrent and residual learning for deforestation monitoring using multitemporal SAR images](https://mlsanity.com/paper/2310.05697/) (2023)
- [Unsupervised Change Detection for Space Habitats Using 3D Point Clouds](https://mlsanity.com/paper/2312.02396/) (2023)
- [IMAFD: An Interpretable Multi-stage Approach to Flood Detection from time series Multispectral Data](https://mlsanity.com/paper/2405.07916/) (2024)

## Abstract (arXiv)

Deep learning (DL) algorithms are considered as a methodology of choice for remote-sensing image analysis over the past few years. Due to its effective applications, deep learning has also been introduced for automatic change detection and achieved great success. The present study attempts to provide a comprehensive review and a meta-analysis of the recent progress in this subfield. Specifically, we first introduce the fundamentals of deep learning methods which arefrequently adopted for change detection. Secondly, we present the details of the meta-analysis conducted to examine the status of change detection DL studies. Then, we focus on deep learning-based change detection methodologies for remote sensing images by giving a general overview of the existing methods. Specifically, these deep learning-based methods were classified into three groups; fully supervised learning-based methods, fully unsupervised learning-based methods and transfer learning-based techniques. As a result of these investigations, promising new directions were identified for future research. This study will contribute in several ways to our understanding of deep learning for change detection and will provide a basis for further research.