# Change Detection Between Optical Remote Sensing Imagery and Map Data via Segment Anything Model (SAM)

arXiv: https://arxiv.org/abs/2401.09019 · HTML: https://mlsanity.com/paper/2401.09019/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 19 references that are in the map.
- First posted on arXiv 17 Jan 2024.

## Builds on (most cited first, 7 of 7)

- [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)
- [The Segment Anything Model (SAM) for Remote Sensing Applications: From Zero to One Shot](https://mlsanity.com/paper/2306.16623/) (2023)
- [Binary Change Guided Hyperspectral Multiclass Change Detection](https://mlsanity.com/paper/2112.04493/) (2021)
- [Exchange means change: an unsupervised single-temporal change detection framework based on intra- and inter-image patch exchange](https://mlsanity.com/paper/2310.00689/) (2023)
- [Unsupervised Multimodal Change Detection Based on Structural Relationship Graph Representation Learning](https://mlsanity.com/paper/2210.00941/) (2022)

## Built on by (most cited first, 12 of 12)

- [Frequency-Assisted Mamba for Remote Sensing Image Super-Resolution](https://mlsanity.com/paper/2405.04964/) (2024)
- [A Unified Framework with Multimodal Fine-tuning for Remote Sensing Semantic Segmentation](https://mlsanity.com/paper/2410.11160/) (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)
- [Performance of Human Annotators in Object Detection and Segmentation of Remotely Sensed Data](https://mlsanity.com/paper/2409.10272/) (2024)
- [S2C: Learning Noise-Resistant Differences for Unsupervised Change Detection in Multimodal Remote Sensing Images](https://mlsanity.com/paper/2502.12604/) (2025)
- [SRMF: A Data Augmentation and Multimodal Fusion Approach for Long-Tail UHR Satellite Image Segmentation](https://mlsanity.com/paper/2504.19839/) (2025)
- [LaVIDE: Language-Prompted Satellite Change Detection via Map-Image Alignment](https://mlsanity.com/paper/2411.19758/) (2024)
- [Information transmission: Inferring change area from change moment in time series remote sensing images](https://mlsanity.com/paper/2509.03112/) (2025)
- [AnomalyCD: A benchmark for Earth anomaly change detection with high-resolution and time-series observations](https://mlsanity.com/paper/2409.05679/) (2024)
- [Operational Change Detection for Geographical Information: Overview and Challenges](https://mlsanity.com/paper/2503.14109/) (2025)
- [Exploring Generalizable Pre-training for Real-world Change Detection via Geometric Estimation](https://mlsanity.com/paper/2504.14306/) (2025)
- [MergeSAM: Unsupervised change detection of remote sensing images based on the Segment Anything Model](https://mlsanity.com/paper/2507.22675/) (2025)

## Abstract (arXiv)

Unsupervised multimodal change detection is pivotal for time-sensitive tasks and comprehensive multi-temporal Earth monitoring. In this study, we explore unsupervised multimodal change detection between two key remote sensing data sources: optical high-resolution imagery and OpenStreetMap (OSM) data. Specifically, we propose to utilize the vision foundation model Segmentation Anything Model (SAM), for addressing our task. Leveraging SAM's exceptional zero-shot transfer capability, high-quality segmentation maps of optical images can be obtained. Thus, we can directly compare these two heterogeneous data forms in the so-called segmentation domain. We then introduce two strategies for guiding SAM's segmentation process: the 'no-prompt' and 'box/mask prompt' methods. The two strategies are designed to detect land-cover changes in general scenarios and to identify new land-cover objects within existing backgrounds, respectively. Experimental results on three datasets indicate that the proposed approach can achieve more competitive results compared to representative unsupervised multimodal change detection methods.