# Time Travelling Pixels: Bitemporal Features Integration with Foundation Model for Remote Sensing Image Change Detection

arXiv: https://arxiv.org/abs/2312.16202 · HTML: https://mlsanity.com/paper/2312.16202/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 23 references that are in the map.
- First posted on arXiv 23 Dec 2023.

## Builds on (most cited first, 11 of 11)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (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)
- [SpectralGPT: Spectral Remote Sensing Foundation Model](https://mlsanity.com/paper/2311.07113/) (2023)
- [A Transformer-Based Siamese Network for Change Detection](https://mlsanity.com/paper/2201.01293/) (2022)
- [RSPrompter: Learning to Prompt for Remote Sensing Instance Segmentation based on Visual Foundation Model](https://mlsanity.com/paper/2306.16269/) (2023)
- [Building Change Detection for Remote Sensing Images Using a Dual Task Constrained Deep Siamese Convolutional Network Model](https://mlsanity.com/paper/1909.07726/) (2019)
- [Continuous Remote Sensing Image Super-Resolution based on Context Interaction in Implicit Function Space](https://mlsanity.com/paper/2302.08046/) (2023)
- [Continuous Cross-resolution Remote Sensing Image Change Detection](https://mlsanity.com/paper/2305.14722/) (2023)

## Built on by (most cited first, 27 of 27)

- [PANGAEA: A Global and Inclusive Benchmark for Geospatial Foundation Models](https://mlsanity.com/paper/2412.04204/) (2024)
- [MTP: Advancing Remote Sensing Foundation Model via Multi-Task Pretraining](https://mlsanity.com/paper/2403.13430/) (2024)
- [Change-Agent: Towards Interactive Comprehensive Remote Sensing Change Interpretation and Analysis](https://mlsanity.com/paper/2403.19646/) (2024)
- [PointSAM: Pointly-Supervised Segment Anything Model for Remote Sensing Images](https://mlsanity.com/paper/2409.13401/) (2024)
- [Remote Sensing SpatioTemporal Vision-Language Models: A Comprehensive Survey](https://mlsanity.com/paper/2412.02573/) (2024)
- [DynamicEarth: How Far are We from Open-Vocabulary Change Detection?](https://mlsanity.com/paper/2501.12931/) (2025)
- [Semantic-CC: Boosting Remote Sensing Image Change Captioning via Foundational Knowledge and Semantic Guidance](https://mlsanity.com/paper/2407.14032/) (2024)
- [RSRefSeg 2: Decoupling Referring Remote Sensing Image Segmentation with Foundation Models](https://mlsanity.com/paper/2507.06231/) (2025)
- [RSBuilding: Towards General Remote Sensing Image Building Extraction and Change Detection with Foundation Model](https://mlsanity.com/paper/2403.07564/) (2024)
- [Open-CD: A Comprehensive Toolbox for Change Detection](https://mlsanity.com/paper/2407.15317/) (2024)
- [Parameter-Efficient Adaptation of Geospatial Foundation Models through Embedding Deflection](https://mlsanity.com/paper/2503.09493/) (2025)
- [UniChange: Unifying Change Detection with Multimodal Large Language Model](https://mlsanity.com/paper/2511.02607/) (2025)
- [JL1-CD: A New Benchmark for Remote Sensing Change Detection and a Robust Multi-Teacher Knowledge Distillation Framework](https://mlsanity.com/paper/2502.13407/) (2025)
- [TaCo: Capturing Spatio-Temporal Semantic Consistency in Remote Sensing Change Detection](https://mlsanity.com/paper/2511.20306/) (2025)
- [Exploring Foundation Models in Remote Sensing Image Change Detection: A Comprehensive Survey](https://mlsanity.com/paper/2410.07824/) (2024)
- [MV-CC: Mask Enhanced Video Model for Remote Sensing Change Caption](https://mlsanity.com/paper/2410.23946/) (2024)
- [M2CD: A Unified MultiModal Framework for Optical-SAR Change Detection with Mixture of Experts and Self-Distillation](https://mlsanity.com/paper/2503.19406/) (2025)
- [MarsSeg: Mars Surface Semantic Segmentation with Multi-level Extractor and Connector](https://mlsanity.com/paper/2404.04155/) (2024)
- [SAM-Based Building Change Detection with Distribution-Aware Fourier Adaptation and Edge-Constrained Warping](https://mlsanity.com/paper/2504.12619/) (2025)
- [Detect Changes like Humans: Incorporating Semantic Priors for Improved Change Detection](https://mlsanity.com/paper/2412.16918/) (2024)
- [Compress-Align-Detect: onboard change detection from unregistered images](https://mlsanity.com/paper/2507.15578/) (2025)
- [HCDN: A Change Detection Network for Construction Housekeeping Using Feature Fusion and Large Vision Models](https://mlsanity.com/paper/2410.17513/) (2024)
- [Multi-Modal Building Change Detection for Large-Scale Small Changes: Benchmark and Baseline](https://mlsanity.com/paper/2603.19077/) (2026)
- [HiSem: Hierarchical Semantic Disentangling for Remote Sensing Image Change Captioning](https://mlsanity.com/paper/2605.15024/) (2026)
- [CoSA: Correlation-Guided Change Attention with Learnable Residual Gating for Remote Sensing Change Detection](https://mlsanity.com/paper/2606.21932/) (2026)
- [C3-Bench: A Context-Aware Change Captioning Benchmark](https://mlsanity.com/paper/2606.25445/) (2026)
- [UrbanCDNet: Appearance-Robust and Boundary-Aware Bitemporal Change Detection for Korean Urban Building Monitoring](https://mlsanity.com/paper/2606.29781/) (2026)

## Abstract (arXiv)

Change detection, a prominent research area in remote sensing, is pivotal in observing and analyzing surface transformations. Despite significant advancements achieved through deep learning-based methods, executing high-precision change detection in spatio-temporally complex remote sensing scenarios still presents a substantial challenge. The recent emergence of foundation models, with their powerful universality and generalization capabilities, offers potential solutions. However, bridging the gap of data and tasks remains a significant obstacle. In this paper, we introduce Time Travelling Pixels (TTP), a novel approach that integrates the latent knowledge of the SAM foundation model into change detection. This method effectively addresses the domain shift in general knowledge transfer and the challenge of expressing homogeneous and heterogeneous characteristics of multi-temporal images. The state-of-the-art results obtained on the LEVIR-CD underscore the efficacy of the TTP. The Code is available at urlhttps://kychen.me/TTP.