# Adapting Segment Anything Model for Change Detection in HR Remote Sensing Images

arXiv: https://arxiv.org/abs/2309.01429 · HTML: https://mlsanity.com/paper/2309.01429/

## Key facts

- Cited by 56 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 53 references that are in the map.
- First posted on arXiv 4 Sep 2023.

## Builds on (most cited first, 26 of 26)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [An Overview of Multi-Task Learning in Deep Neural Networks](https://mlsanity.com/paper/1706.05098/) (2017)
- [Segment Everything Everywhere All at Once](https://mlsanity.com/paper/2304.06718/) (2023)
- [Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation](https://mlsanity.com/paper/2304.12620/) (2023)
- [Faster Segment Anything: Towards Lightweight SAM for Mobile Applications](https://mlsanity.com/paper/2306.14289/) (2023)
- [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)
- [Fast Segment Anything](https://mlsanity.com/paper/2306.12156/) (2023)
- [A Transformer-Based Siamese Network for Change Detection](https://mlsanity.com/paper/2201.01293/) (2022)
- [SegGPT: Segmenting Everything In Context](https://mlsanity.com/paper/2304.03284/) (2023)
- [Revisiting Weak-to-Strong Consistency in Semi-Supervised Semantic Segmentation](https://mlsanity.com/paper/2208.09910/) (2022)
- [Semi-Supervised Semantic Segmentation with High- and Low-level Consistency](https://mlsanity.com/paper/1908.05724/) (2019)
- [Segment Anything Is Not Always Perfect: An Investigation of SAM on Different Real-world Applications](https://mlsanity.com/paper/2304.05750/) (2023)
- [The Segment Anything Model (SAM) for Remote Sensing Applications: From Zero to One Shot](https://mlsanity.com/paper/2306.16623/) (2023)
- [S2Looking: A Satellite Side-Looking Dataset for Building Change Detection](https://mlsanity.com/paper/2107.09244/) (2021)
- [DASNet: Dual attentive fully convolutional siamese networks for change detection of high resolution satellite images](https://mlsanity.com/paper/2003.03608/) (2020)
- [Learning Spectral-Spatial-Temporal Features via a Recurrent Convolutional Neural Network for Change Detection in Multispectral Imagery](https://mlsanity.com/paper/1803.02642/) (2018)
- [Bi-Temporal Semantic Reasoning for the Semantic Change Detection in HR Remote Sensing Images](https://mlsanity.com/paper/2108.06103/) (2021)
- [Joint Spatio-Temporal Modeling for the Semantic Change Detection in Remote Sensing Images](https://mlsanity.com/paper/2212.05245/) (2022)
- [Change Detection Methods for Remote Sensing in the Last Decade: A Comprehensive Review](https://mlsanity.com/paper/2305.05813/) (2023)
- [Revisiting Consistency Regularization for Semi-supervised Change Detection in Remote Sensing Images](https://mlsanity.com/paper/2204.08454/) (2022)
- [Self-Pair: Synthesizing Changes from Single Source for Object Change Detection in Remote Sensing Imagery](https://mlsanity.com/paper/2212.10236/) (2022)
- [A Multiscale Graph Convolutional Network for Change Detection in Homogeneous and Heterogeneous Remote Sensing Images](https://mlsanity.com/paper/2102.08041/) (2021)
- [DiResNet: Direction-aware Residual Network for Road Extraction in VHR Remote Sensing Images](https://mlsanity.com/paper/2005.07232/) (2020)

## Built on by (most cited first, 30 of 56)

- [A New Learning Paradigm for Foundation Model-based Remote Sensing Change Detection](https://mlsanity.com/paper/2312.01163/) (2023)
- [SAM-Assisted Remote Sensing Imagery Semantic Segmentation with Object and Boundary Constraints](https://mlsanity.com/paper/2312.02464/) (2023)
- [Segment Any Change](https://mlsanity.com/paper/2402.01188/) (2024)
- [PointSAM: Pointly-Supervised Segment Anything Model for Remote Sensing Images](https://mlsanity.com/paper/2409.13401/) (2024)
- [Towards Generalizable Scene Change Detection](https://mlsanity.com/paper/2409.06214/) (2024)
- [A Unified Framework with Multimodal Fine-tuning for Remote Sensing Semantic Segmentation](https://mlsanity.com/paper/2410.11160/) (2024)
- [3DGS-CD: 3D Gaussian Splatting-based Change Detection for Physical Object Rearrangement](https://mlsanity.com/paper/2411.03706/) (2024)
- [When Geoscience Meets Foundation Models: Towards General Geoscience Artificial Intelligence System](https://mlsanity.com/paper/2309.06799/) (2023)
- [Adapting the Segment Anything Model During Usage in Novel Situations](https://mlsanity.com/paper/2404.08421/) (2024)
- [MaskCD: A Remote Sensing Change Detection Network Based on Mask Classification](https://mlsanity.com/paper/2404.12081/) (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)
- [A Multispectral Automated Transfer Technique (MATT) for machine-driven image labeling utilizing the Segment Anything Model (SAM)](https://mlsanity.com/paper/2402.11413/) (2024)
- [UniChange: Unifying Change Detection with Multimodal Large Language Model](https://mlsanity.com/paper/2511.02607/) (2025)
- [GeoSAM: Fine-tuning SAM with Multi-Modal Prompts for Mobility Infrastructure Segmentation](https://mlsanity.com/paper/2311.11319/) (2023)
- [Stable Segment Anything Model](https://mlsanity.com/paper/2311.15776/) (2023)
- [LCD-Net: A Lightweight Remote Sensing Change Detection Network Combining Feature Fusion and Gating Mechanism](https://mlsanity.com/paper/2410.11580/) (2024)
- [PeftCD: Leveraging Vision Foundation Models with Parameter-Efficient Fine-Tuning for Remote Sensing Change Detection](https://mlsanity.com/paper/2509.09572/) (2025)
- [Auto-Prompting SAM for Weakly Supervised Landslide Extraction](https://mlsanity.com/paper/2501.13426/) (2025)
- [OmniOVCD: Streamlining Open-Vocabulary Change Detection with SAM 3](https://mlsanity.com/paper/2601.13895/) (2026)
- [Segment Any-Quality Images with Generative Latent Space Enhancement](https://mlsanity.com/paper/2503.12507/) (2025)
- [Survey of Multimodal Geospatial Foundation Models: Techniques, Applications, and Challenges](https://mlsanity.com/paper/2510.22964/) (2025)
- [DEAL-300K: Diffusion-based Editing Area Localization with a 300K-Scale Dataset and Frequency-Prompted Baseline](https://mlsanity.com/paper/2511.23377/) (2025)
- [Boosting Segment Anything Model to Generalize Visually Non-Salient Scenarios](https://mlsanity.com/paper/2601.00537/) (2026)
- [Zero-Shot Pupil Segmentation with SAM 2: A Case Study of Over 14 Million Images](https://mlsanity.com/paper/2410.08926/) (2024)
- [S2C: Learning Noise-Resistant Differences for Unsupervised Change Detection in Multimodal Remote Sensing Images](https://mlsanity.com/paper/2502.12604/) (2025)
- [SAM-Based Building Change Detection with Distribution-Aware Fourier Adaptation and Edge-Constrained Warping](https://mlsanity.com/paper/2504.12619/) (2025)
- [SRMF: A Data Augmentation and Multimodal Fusion Approach for Long-Tail UHR Satellite Image Segmentation](https://mlsanity.com/paper/2504.19839/) (2025)
- [Changes in Gaza: DINOv3-Powered Multi-Class Change Detection for Damage Assessment in Conflict Zones](https://mlsanity.com/paper/2511.19035/) (2025)
- [Zero-Shot Refinement of Buildings' Segmentation Models using SAM](https://mlsanity.com/paper/2310.01845/) (2023)
- [VideoSAM: A Large Vision Foundation Model for High-Speed Video Segmentation](https://mlsanity.com/paper/2410.21304/) (2024)

## Abstract (arXiv)

Vision Foundation Models (VFMs) such as the Segment Anything Model (SAM) allow zero-shot or interactive segmentation of visual contents, thus they are quickly applied in a variety of visual scenes. However, their direct use in many Remote Sensing (RS) applications is often unsatisfactory due to the special imaging characteristics of RS images. In this work, we aim to utilize the strong visual recognition capabilities of VFMs to improve the change detection of high-resolution Remote Sensing Images (RSIs). We employ the visual encoder of FastSAM, an efficient variant of the SAM, to extract visual representations in RS scenes. To adapt FastSAM to focus on some specific ground objects in the RS scenes, we propose a convolutional adaptor to aggregate the task-oriented change information. Moreover, to utilize the semantic representations that are inherent to SAM features, we introduce a task-agnostic semantic learning branch to model the semantic latent in bi-temporal RSIs. The resulting method, SAMCD, obtains superior accuracy compared to the SOTA methods and exhibits a sample-efficient learning ability that is comparable to semi-supervised CD methods. To the best of our knowledge, this is the first work that adapts VFMs for the CD of HR RSIs.