# TaCo: Capturing Spatio-Temporal Semantic Consistency in Remote Sensing Change Detection

arXiv: https://arxiv.org/abs/2511.20306 · HTML: https://mlsanity.com/paper/2511.20306/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 48 references that are in the map.
- First posted on arXiv 25 Nov 2025.

## Builds on (most cited first, 21 of 21)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://mlsanity.com/paper/2312.00752/) (2023)
- [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://mlsanity.com/paper/2105.15203/) (2021)
- [DINOv3](https://mlsanity.com/paper/2508.10104/) (2025)
- [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)
- [ChangeMamba: Remote Sensing Change Detection With Spatiotemporal State Space Model](https://mlsanity.com/paper/2404.03425/) (2024)
- [RS-Mamba for Large Remote Sensing Image Dense Prediction](https://mlsanity.com/paper/2404.02668/) (2024)
- [CDMamba: Incorporating Local Clues into Mamba for Remote Sensing Image Binary Change Detection](https://mlsanity.com/paper/2406.04207/) (2024)
- [Text2Earth: Unlocking Text-driven Remote Sensing Image Generation with a Global-Scale Dataset and a Foundation Model](https://mlsanity.com/paper/2501.00895/) (2025)
- [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)
- [Building Disaster Damage Assessment in Satellite Imagery with Multi-Temporal Fusion](https://mlsanity.com/paper/2004.05525/) (2020)
- [Time Travelling Pixels: Bitemporal Features Integration with Foundation Model for Remote Sensing Image Change Detection](https://mlsanity.com/paper/2312.16202/) (2023)
- [Remote Sensing SpatioTemporal Vision-Language Models: A Comprehensive Survey](https://mlsanity.com/paper/2412.02573/) (2024)
- [FoBa: A Foreground-Background co-Guided Method and New Benchmark for Remote Sensing Semantic Change Detection](https://mlsanity.com/paper/2509.15788/) (2025)
- [A Late-Stage Bitemporal Feature Fusion Network for Semantic Change Detection](https://mlsanity.com/paper/2406.10678/) (2024)
- [PeftCD: Leveraging Vision Foundation Models with Parameter-Efficient Fine-Tuning for Remote Sensing Change Detection](https://mlsanity.com/paper/2509.09572/) (2025)
- [CDXLSTM: Boosting Remote Sensing Change Detection with Extended Long Short-Term Memory](https://mlsanity.com/paper/2411.07863/) (2024)

## Built on by (most cited first, 6 of 6)

- [HiSem: Hierarchical Semantic Disentangling for Remote Sensing Image Change Captioning](https://mlsanity.com/paper/2605.15024/) (2026)
- [ChangeFlow -- Latent Rectified Flow for Change Detection in Remote Sensing](https://mlsanity.com/paper/2605.15375/) (2026)
- [SFR-Net: Learning Scale-Frustum Representations for Ultra-Wide Area Remote Sensing Image Segmentation](https://mlsanity.com/paper/2605.25737/) (2026)
- [SemDINO: Foundation Prior-Guided Cross-Temporal Semantic Alignment Network for Remote Sensing Change Detection](https://mlsanity.com/paper/2606.09772/) (2026)
- [FootprintNet: State-Transition-Guided Dynamic Footprint Learning for Multi-temporal Remote Sensing Change Detection](https://mlsanity.com/paper/2607.27969/) (2026)
- [Remote Sensing Sparse-View 3D Gaussian Splatting via Depth Image-Based Rendering](https://mlsanity.com/paper/2609.35612/) (2026)

## Abstract (arXiv)

Remote sensing change detection (RSCD) aims to identify surface changes across bi-temporal satellite images. Most previous methods rely solely on mask supervision, which effectively guides spatial localization but provides limited constraints on the temporal semantic transitions. Consequently, they often produce spatially coherent predictions while still suffering from unresolved semantic inconsistencies. To address this limitation, we propose TaCo, a spatio-temporal semantic consistent network, which enriches the existing mask-supervised framework with a spatio-temporal semantic joint constraint. TaCo conceptualizes change as a semantic transition between bi-temporal states, in which one temporal feature representation can be derived from the other via dedicated transition features. To realize this, we introduce a Text-guided Transition Generator that integrates textual semantics with bi-temporal visual features to construct the cross-temporal transition features. In addition, we propose a spatio-temporal semantic joint constraint consisting of bi-temporal reconstruct constraints and a transition constraint: the former enforces alignment between reconstructed and original features, while the latter enhances discrimination for changes. This design can yield substantial performance gains without introducing any additional computational overhead during inference. Extensive experiments on six public datasets, spanning both binary and semantic change detection tasks, demonstrate that TaCo consistently achieves SOTA performance.