# RemoteVAR: Autoregressive Visual Modeling for Remote Sensing Change Detection

arXiv: https://arxiv.org/abs/2601.11898 · HTML: https://mlsanity.com/paper/2601.11898/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 31 references that are in the map.
- First posted on arXiv 17 Jan 2026.

## Builds on (most cited first, 14 of 14)

- [Visual Autoregressive Modeling: Scalable Image Generation via Next-Scale Prediction](https://mlsanity.com/paper/2404.02905/) (2024)
- [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)
- [CMX: Cross-Modal Fusion for RGB-X Semantic Segmentation with Transformers](https://mlsanity.com/paper/2203.04838/) (2022)
- [Seasonal Contrast: Unsupervised Pre-Training from Uncurated Remote Sensing Data](https://mlsanity.com/paper/2103.16607/) (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)
- [RS-Mamba for Large Remote Sensing Image Dense Prediction](https://mlsanity.com/paper/2404.02668/) (2024)
- [S2Looking: A Satellite Side-Looking Dataset for Building Change Detection](https://mlsanity.com/paper/2107.09244/) (2021)
- [ControlVAR: Exploring Controllable Visual Autoregressive Modeling](https://mlsanity.com/paper/2406.09750/) (2024)
- [Deep Learning for Change Detection in Remote Sensing Images: Comprehensive Review and Meta-Analysis](https://mlsanity.com/paper/2006.05612/) (2020)
- [GCD-DDPM: A Generative Change Detection Model Based on Difference-Feature Guided DDPM](https://mlsanity.com/paper/2306.03424/) (2023)
- [DDPM-CD: Denoising Diffusion Probabilistic Models as Feature Extractors for Change Detection](https://mlsanity.com/paper/2206.11892/) (2022)
- [Semantic-aware Dense Representation Learning for Remote Sensing Image Change Detection](https://mlsanity.com/paper/2205.13769/) (2022)

## Built on by (most cited first, 1 of 1)

- [Freq-RemoteVAR: Next-Frequency Autoregressive Modeling for Remote Sensing Change Detection](https://mlsanity.com/paper/2607.25815/) (2026)

## Abstract (arXiv)

Remote sensing change detection aims to localize and characterize scene changes between two time points and is central to applications such as environmental monitoring and disaster assessment. Meanwhile, visual autoregressive models (VARs) have recently shown impressive image generation capability, but their adoption for pixel-level discriminative tasks remains limited due to weak controllability, suboptimal dense prediction performance and exposure bias. We introduce RemoteVAR, a new VAR-based change detection framework that addresses these limitations by conditioning autoregressive prediction on multi-resolution fused bi-temporal features via cross-attention, and by employing an autoregressive training strategy designed specifically for change map prediction. Extensive experiments on standard change detection benchmarks show that RemoteVAR delivers consistent and significant improvements over strong diffusion-based and transformer-based baselines, establishing a competitive autoregressive alternative for remote sensing change detection. Code will be available hrefhttps://github.com/yilmazkorkmaz1/RemoteVARunderlinehere.