mlsanityData to 14 Oct 2026

Exploring Foundation Models in Remote Sensing Image Change Detection: A Comprehensive Survey

arXiv 2410.07824 ↗First posted 10 Oct 2024cs.CVcited by 3

Not on the map. Why?

Abstract (arXiv)

Change detection, as an important and widely applied technique in the field of remote sensing, aims to analyze changes in surface areas over time and has broad applications in areas such as environmental monitoring, urban development, and land use analysis.In recent years, deep learning, especially the development of foundation models, has provided more powerful solutions for feature extraction and data fusion, effectively addressing these complexities. This paper systematically reviews the latest advancements in the field of change detection, with a focus on the application of foundation models in remote sensing tasks.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 59 of its 136 references that are in the map. First posted on arXiv 10 Oct 2024.

Builds on

59 of 136 references.

References · 59

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
  2. 2020Language Models are Few-Shot Learnerscited by 32,419
  3. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,186
  4. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
  5. 2023Segment Anythingcited by 8,234
  6. 2022Generative Adversarial Networkscited by 5,627
  7. 2022BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generationcited by 4,322
  8. 2023A Survey of Large Language Modelscited by 2,576
  9. 2019ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Taskscited by 2,222
  10. 2023Vision-Language Models for Vision Tasks: A Surveycited by 839
  11. 2020Self-supervised Learning: Generative or Contrastivecited by 694
  12. 2021CLIP4Clip: An Empirical Study of CLIP for End to End Video Clip Retrievalcited by 651
38 more
  1. 2022Multimodal Learning with Transformers: A Surveycited by 400
  2. 2023A Comprehensive Survey on Pretrained Foundation Models: A History from BERT to ChatGPTcited by 368
  3. 2023RemoteCLIP: A Vision Language Foundation Model for Remote Sensingcited by 363
  4. 2019Dynamic Convolution: Attention over Convolution Kernelscited by 356
  5. 2017Functional Map of the Worldcited by 338
  6. 2021Remote Sensing Image Change Detection with Transformerscited by 306
  7. 2022X-CLIP: End-to-End Multi-grained Contrastive Learning for Video-Text Retrievalcited by 280
  8. 2023Multimodal Deep Learningcited by 258
  9. 2023SpectralGPT: Spectral Remote Sensing Foundation Modelcited by 252
  10. 2022A Transformer-Based Siamese Network for Change Detectioncited by 244
  11. 2023SkySense: A Multi-Modal Remote Sensing Foundation Model Towards Universal Interpretation for Earth Observation Imagerycited by 205
  12. 2023RSPrompter: Learning to Prompt for Remote Sensing Instance Segmentation based on Visual Foundation Modelcited by 181
  13. 2023Knowledge-enhanced Visual-Language Pre-training on Chest Radiology Imagescited by 128
  14. 2024Vision-Language Models for Medical Report Generation and Visual Question Answering: A Reviewcited by 125
  15. 2024ChangeMamba: Remote Sensing Change Detection With Spatiotemporal State Space Modelcited by 120
  16. 2021Towards artificial general intelligence via a multimodal foundation modelcited by 112
  17. 2020A review: Deep learning for medical image segmentation using multi-modality fusioncited by 84
  18. 2024HyperSIGMA: Hyperspectral Intelligence Comprehension Foundation Modelcited by 73
  19. 2019Self-Supervised Learning for Cardiac MR Image Segmentation by Anatomical Position Predictioncited by 70
  20. 2021Change is Everywhere: Single-Temporal Supervised Object Change Detection in Remote Sensing Imagerycited by 64
  21. 2024Change-Agent: Towards Interactive Comprehensive Remote Sensing Change Interpretation and Analysiscited by 63
  22. 2023A Billion-scale Foundation Model for Remote Sensing Imagescited by 62
  23. 2023Drive Anywhere: Generalizable End-to-end Autonomous Driving with Multi-modal Foundation Modelscited by 59
  24. 2023Large Language Models are Visual Reasoning Coordinatorscited by 58
  25. 2024RS-Agent: Automating Remote Sensing Tasks through Intelligent Agentcited by 49
  26. 2023SurgicalGPT: End-to-End Language-Vision GPT for Visual Question Answering in Surgerycited by 48
  27. 2023A New Learning Paradigm for Foundation Model-based Remote Sensing Change Detectioncited by 47
  28. 2023Change Detection Methods for Remote Sensing in the Last Decade: A Comprehensive Reviewcited by 41
  29. 2024Changen2: Multi-Temporal Remote Sensing Generative Change Foundation Modelcited by 37
  30. 2020PGL: Prior-Guided Local Self-supervised Learning for 3D Medical Image Segmentationcited by 36
  31. 2020Semantic Change Detection with Asymmetric Siamese Networkscited by 31
  32. 2024UniBench: Visual Reasoning Requires Rethinking Vision-Language Beyond Scalingcited by 31
  33. 2023Time Travelling Pixels: Bitemporal Features Integration with Foundation Model for Remote Sensing Image Change Detectioncited by 27
  34. 2022Self-Supervised Pretraining for 2D Medical Image Segmentationcited by 24
  35. 2023GCD-DDPM: A Generative Change Detection Model Based on Difference-Feature Guided DDPMcited by 21
  36. 2023Exchanging Dual Encoder-Decoder: A New Strategy for Change Detection with Semantic Guidance and Spatial Localizationcited by 21
  37. 2023Segment Change Model (SCM) for Unsupervised Change detection in VHR Remote Sensing Images: a Case Study of Buildingscited by 21
  38. 2024ELGC-Net: Efficient Local-Global Context Aggregation for Remote Sensing Change Detectioncited by 19

50 most cited of 59 All 59 (JSON)

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 1.

3 papers.

  1. 2025ViLaCD-R1: A Vision-Language Framework for Semantic Change Detection in Remote Sensingcited by 3
  2. 2025MVT: Mask-Grounded Vision-Language Models for Taxonomy-Aligned Land-Cover Taggingcited by 0
  3. 2026Prior-guided Fusion of Multimodal Features for Change Detection from Optical-SAR Imagescited by 0