# Change Detection Methods for Remote Sensing in the Last Decade: A Comprehensive Review

arXiv: https://arxiv.org/abs/2305.05813 · HTML: https://mlsanity.com/paper/2305.05813/

## Key facts

- Cited by 41 papers in the map (data through 14 Oct 2026).
- Builds on 68 of its 237 references that are in the map.
- First posted on arXiv 9 May 2023.

## Builds on (most cited first, 30 of 68)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [Pyramid Scene Parsing Network](https://mlsanity.com/paper/1612.01105/) (2016)
- [Rethinking Atrous Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1706.05587/) (2017)
- [Diffusion Models: A Comprehensive Survey of Methods and Applications](https://mlsanity.com/paper/2209.00796/) (2022)
- [Transformers in Vision: A Survey](https://mlsanity.com/paper/2101.01169/) (2021)
- [Domain Generalization: A Survey](https://mlsanity.com/paper/2103.02503/) (2021)
- [DETRs Beat YOLOs on Real-time Object Detection](https://mlsanity.com/paper/2304.08069/) (2023)
- [Images Speak in Images: A Generalist Painter for In-Context Visual Learning](https://mlsanity.com/paper/2212.02499/) (2022)
- [Fully Convolutional Siamese Networks for Change Detection](https://mlsanity.com/paper/1810.08462/) (2018)
- [Remote Sensing Image Change Detection with Transformers](https://mlsanity.com/paper/2103.00208/) (2021)
- [A Transformer-Based Siamese Network for Change Detection](https://mlsanity.com/paper/2201.01293/) (2022)
- [Attention Mechanisms in Computer Vision: A Survey](https://mlsanity.com/paper/2111.07624/) (2021)
- [SegGPT: Segmenting Everything In Context](https://mlsanity.com/paper/2304.03284/) (2023)
- [PLOP: Learning without Forgetting for Continual Semantic Segmentation](https://mlsanity.com/paper/2011.11390/) (2020)
- [DiffuMask: Synthesizing Images with Pixel-level Annotations for Semantic Segmentation Using Diffusion Models](https://mlsanity.com/paper/2303.11681/) (2023)
- [Deep learning in remote sensing: a review](https://mlsanity.com/paper/1710.03959/) (2017)
- [Semantic Flow for Fast and Accurate Scene Parsing](https://mlsanity.com/paper/2002.10120/) (2020)
- [Transformer-Based Visual Segmentation: A Survey](https://mlsanity.com/paper/2304.09854/) (2023)
- [Attention in Natural Language Processing](https://mlsanity.com/paper/1902.02181/) (2019)
- [Improving Semantic Segmentation via Decoupled Body and Edge Supervision](https://mlsanity.com/paper/2007.10035/) (2020)
- [Involution: Inverting the Inherence of Convolution for Visual Recognition](https://mlsanity.com/paper/2103.06255/) (2021)
- [Building Change Detection for Remote Sensing Images Using a Dual Task Constrained Deep Siamese Convolutional Network Model](https://mlsanity.com/paper/1909.07726/) (2019)
- [Context-Aware Mixup for Domain Adaptive Semantic Segmentation](https://mlsanity.com/paper/2108.03557/) (2021)
- [Video K-Net: A Simple, Strong, and Unified Baseline for Video Segmentation](https://mlsanity.com/paper/2204.04656/) (2022)
- [TransVOD: End-to-End Video Object Detection with Spatial-Temporal Transformers](https://mlsanity.com/paper/2201.05047/) (2022)

## Built on by (most cited first, 30 of 41)

- [TEOChat: A Large Vision-Language Assistant for Temporal Earth Observation Data](https://mlsanity.com/paper/2410.06234/) (2024)
- [Adapting Segment Anything Model for Change Detection in HR Remote Sensing Images](https://mlsanity.com/paper/2309.01429/) (2023)
- [A Survey on Remote Sensing Foundation Models: From Vision to Multimodality](https://mlsanity.com/paper/2503.22081/) (2025)
- [Robust Scene Change Detection Using Visual Foundation Models and Cross-Attention Mechanisms](https://mlsanity.com/paper/2409.16850/) (2024)
- [A Survey of Sample-Efficient Deep Learning for Change Detection in Remote Sensing: Tasks, Strategies, and Challenges](https://mlsanity.com/paper/2502.02835/) (2025)
- [SSLChange: A Self-supervised Change Detection Framework Based on Domain Adaptation](https://mlsanity.com/paper/2405.18224/) (2024)
- [Vision Mamba in Remote Sensing: A Comprehensive Survey of Techniques, Applications and Outlook](https://mlsanity.com/paper/2505.00630/) (2025)
- [A Late-Stage Bitemporal Feature Fusion Network for Semantic Change Detection](https://mlsanity.com/paper/2406.10678/) (2024)
- [FLOGA: A machine learning ready dataset, a benchmark and a novel deep learning model for burnt area mapping with Sentinel-2](https://mlsanity.com/paper/2311.03339/) (2023)
- [ViewDelta: Scaling Scene Change Detection through Text-Conditioning](https://mlsanity.com/paper/2412.07612/) (2024)
- [A Literature Review of Literature Reviews in Pattern Analysis and Machine Intelligence](https://mlsanity.com/paper/2402.12928/) (2024)
- [Towards Temporal Change Explanations from Bi-Temporal Satellite Images](https://mlsanity.com/paper/2407.09548/) (2024)
- [Exploring Foundation Models in Remote Sensing Image Change Detection: A Comprehensive Survey](https://mlsanity.com/paper/2410.07824/) (2024)
- [Foundation Model-Driven Semantic Change Detection in Remote Sensing Imagery](https://mlsanity.com/paper/2602.13780/) (2026)
- [EHCTNet: Enhanced Hybrid of CNN and Transformer Network for Remote Sensing Image Change Detection](https://mlsanity.com/paper/2501.01238/) (2025)
- [Mamba-FCS: Joint Spatio- Frequency Feature Fusion, Change-Guided Attention, and SeK Loss for Enhanced Semantic Change Detection in Remote Sensing](https://mlsanity.com/paper/2508.08232/) (2025)
- [Deep Learning Based Domain Adaptation Methods in Remote Sensing: A Comprehensive Survey](https://mlsanity.com/paper/2510.15615/) (2025)
- [Agentic AI for Remote Sensing: Technical Challenges and Research Directions](https://mlsanity.com/paper/2604.24919/) (2026)
- [DFM: Difference Feature Modeling with Text-Guided Gated Contrastive Loss for Remote Sensing Image Change Captioning](https://mlsanity.com/paper/2606.27410/) (2026)
- [RSICCLLM: A Multimodal Large Language Model for Remote Sensing Image Change Captioning](https://mlsanity.com/paper/2606.28266/) (2026)
- [Cross-Domain Separable Translation Network for Multimodal Image Change Detection](https://mlsanity.com/paper/2407.16158/) (2024)
- [Text-conditioned State Space Model For Domain-generalized Change Detection Visual Question Answering](https://mlsanity.com/paper/2508.08974/) (2025)
- [Remote Sensing Change Detection via Weak Temporal Supervision](https://mlsanity.com/paper/2601.02126/) (2026)
- [ReA-OVCD: Training-Free Open-Vocabulary Change Detection via Semantic-Spatial Reliability Assessment](https://mlsanity.com/paper/2606.20032/) (2026)
- [GTPC-SSCD: Gate-guided Two-level Perturbation Consistency-based Semi-Supervised Change Detection](https://mlsanity.com/paper/2411.18880/) (2024)
- [CFNet: Optimizing Remote Sensing Change Detection through Content-Aware Enhancement](https://mlsanity.com/paper/2503.08505/) (2025)
- [AI-Driven Tactical Communications and Networking for Defense: A Survey and Emerging Trends](https://mlsanity.com/paper/2504.05071/) (2025)
- [HSACNet: Hierarchical Scale-Aware Consistency Regularized Semi-Supervised Change Detection](https://mlsanity.com/paper/2504.13428/) (2025)
- [CEBSNet: Change-Excited and Background-Suppressed Network with Temporal Dependency Modeling for Bitemporal Change Detection](https://mlsanity.com/paper/2505.15322/) (2025)
- [Label-frugal satellite image change detection with generative virtual exemplar learning](https://mlsanity.com/paper/2510.06926/) (2025)

## Abstract (arXiv)

Change detection is an essential and widely utilized task in remote sensing that aims to detect and analyze changes occurring in the same geographical area over time, which has broad applications in urban development, agricultural surveys, and land cover monitoring. Detecting changes in remote sensing images is a complex challenge due to various factors, including variations in image quality, noise, registration errors, illumination changes, complex landscapes, and spatial heterogeneity. In recent years, deep learning has emerged as a powerful tool for feature extraction and addressing these challenges. Its versatility has resulted in its widespread adoption for numerous image-processing tasks. This paper presents a comprehensive survey of significant advancements in change detection for remote sensing images over the past decade. We first introduce some preliminary knowledge for the change detection task, such as problem definition, datasets, evaluation metrics, and transformer basics, as well as provide a detailed taxonomy of existing algorithms from three different perspectives: algorithm granularity, supervision modes, and learning frameworks in the methodology section. This survey enables readers to gain systematic knowledge of change detection tasks from various angles. We then summarize the state-of-the-art performance on several dominant change detection datasets, providing insights into the strengths and limitations of existing algorithms. Based on our survey, some future research directions for change detection in remote sensing are well identified. This survey paper will shed some light on the community and inspire further research efforts in the change detection task.