# DynamicEarth: How Far are We from Open-Vocabulary Change Detection?

arXiv: https://arxiv.org/abs/2501.12931 · HTML: https://mlsanity.com/paper/2501.12931/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 50 of its 80 references that are in the map.
- First posted on arXiv 22 Jan 2025.

## Builds on (most cited first, 30 of 50)

- [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)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [Visual Instruction Tuning](https://mlsanity.com/paper/2304.08485/) (2023)
- [Improved Baselines with Visual Instruction Tuning](https://mlsanity.com/paper/2310.03744/) (2023)
- [Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection](https://mlsanity.com/paper/2303.05499/) (2023)
- [Sigmoid Loss for Language Image Pre-Training](https://mlsanity.com/paper/2303.15343/) (2023)
- [SAM 2: Segment Anything in Images and Videos](https://mlsanity.com/paper/2408.00714/) (2024)
- [Masked-attention Mask Transformer for Universal Image Segmentation](https://mlsanity.com/paper/2112.01527/) (2021)
- [DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection](https://mlsanity.com/paper/2203.03605/) (2022)
- [Language-driven Semantic Segmentation](https://mlsanity.com/paper/2201.03546/) (2022)
- [GroupViT: Semantic Segmentation Emerges from Text Supervision](https://mlsanity.com/paper/2202.11094/) (2022)
- [Florence-2: Advancing a Unified Representation for a Variety of Vision Tasks](https://mlsanity.com/paper/2311.06242/) (2023)
- [Segment Anything in High Quality](https://mlsanity.com/paper/2306.01567/) (2023)
- [Faster Segment Anything: Towards Lightweight SAM for Mobile Applications](https://mlsanity.com/paper/2306.14289/) (2023)
- [Side Adapter Network for Open-Vocabulary Semantic Segmentation](https://mlsanity.com/paper/2302.12242/) (2023)
- [Fast Segment Anything](https://mlsanity.com/paper/2306.12156/) (2023)
- [CAT-Seg: Cost Aggregation for Open-Vocabulary Semantic Segmentation](https://mlsanity.com/paper/2303.11797/) (2023)
- [RSPrompter: Learning to Prompt for Remote Sensing Instance Segmentation based on Visual Foundation Model](https://mlsanity.com/paper/2306.16269/) (2023)
- [SCLIP: Rethinking Self-Attention for Dense Vision-Language Inference](https://mlsanity.com/paper/2312.01597/) (2023)
- [Grounding DINO 1.5: Advance the "Edge" of Open-Set Object Detection](https://mlsanity.com/paper/2405.10300/) (2024)
- [xGen-MM (BLIP-3): A Family of Open Large Multimodal Models](https://mlsanity.com/paper/2408.08872/) (2024)
- [ClearCLIP: Decomposing CLIP Representations for Dense Vision-Language Inference](https://mlsanity.com/paper/2407.12442/) (2024)
- [ProxyCLIP: Proxy Attention Improves CLIP for Open-Vocabulary Segmentation](https://mlsanity.com/paper/2408.04883/) (2024)
- [RSMamba: Remote Sensing Image Classification with State Space Model](https://mlsanity.com/paper/2403.19654/) (2024)
- [DINO-X: A Unified Vision Model for Open-World Object Detection and Understanding](https://mlsanity.com/paper/2411.14347/) (2024)
- [Changer: Feature Interaction is What You Need for Change Detection](https://mlsanity.com/paper/2209.08290/) (2022)
- [S2Looking: A Satellite Side-Looking Dataset for Building Change Detection](https://mlsanity.com/paper/2107.09244/) (2021)

## Built on by (most cited first, 21 of 21)

- [Designing Domain-Specific Agents via Hierarchical Task Abstraction Mechanism](https://mlsanity.com/paper/2511.17198/) (2025)
- [SegEarth-OV3: Exploring SAM 3 for Open-Vocabulary Semantic Segmentation in Remote Sensing Images](https://mlsanity.com/paper/2512.08730/) (2025)
- [SegEarth-R2: Towards Comprehensive Language-guided Segmentation for Remote Sensing Images](https://mlsanity.com/paper/2512.20013/) (2025)
- [Annotation-Free Open-Vocabulary Segmentation for Remote-Sensing Images](https://mlsanity.com/paper/2508.18067/) (2025)
- [UniVCD: A New Method for Unsupervised Change Detection in the Open-Vocabulary Era](https://mlsanity.com/paper/2512.13089/) (2025)
- [DGTRSD & DGTRS-CLIP: A Dual-Granularity Remote Sensing Image-Text Dataset and Vision Language Foundation Model for Alignment](https://mlsanity.com/paper/2503.19311/) (2025)
- [OmniOVCD: Streamlining Open-Vocabulary Change Detection with SAM 3](https://mlsanity.com/paper/2601.13895/) (2026)
- [Seg2Change: Adapting Open-Vocabulary Semantic Segmentation Model for Remote Sensing Change Detection](https://mlsanity.com/paper/2604.11231/) (2026)
- [OpenDPR: Open-Vocabulary Change Detection via Vision-Centric Diffusion-Guided Prototype Retrieval for Remote Sensing Imagery](https://mlsanity.com/paper/2603.27645/) (2026)
- [CoRegOVCD: Consistency-Regularized Open-Vocabulary Change Detection](https://mlsanity.com/paper/2604.02160/) (2026)
- [MemOVCD: Training-Free Open-Vocabulary Change Detection via Cross-Temporal Memory Reasoning and Global-Local Adaptive Rectification](https://mlsanity.com/paper/2604.26774/) (2026)
- [An Open-Source Benchmark and Baseline for Multi-temporal Referring Segmentation](https://mlsanity.com/paper/2606.00987/) (2026)
- [ReA-OVCD: Training-Free Open-Vocabulary Change Detection via Semantic-Spatial Reliability Assessment](https://mlsanity.com/paper/2606.20032/) (2026)
- [Exploring Generalizable Pre-training for Real-world Change Detection via Geometric Estimation](https://mlsanity.com/paper/2504.14306/) (2025)
- [Improving Few-Shot Change Detection Visual Question Answering via Decision-Ambiguity-guided Reinforcement Fine-Tuning](https://mlsanity.com/paper/2512.24591/) (2025)
- [Make Some Noise: Unsupervised Remote Sensing Change Detection Using Latent Space Perturbations](https://mlsanity.com/paper/2602.19881/) (2026)
- [Spatially Selective Self-Training for Unsupervised Building Change Detection](https://mlsanity.com/paper/2606.10775/) (2026)
- [JL1-CC&QA: Extending the JL1-CD Benchmark with Change Captioning and Question Answering](https://mlsanity.com/paper/2606.31745/) (2026)
- [CogVis: Must Open-Vocabulary Change Detection Perceive the Scene Anew for Every Query?](https://mlsanity.com/paper/2608.06150/) (2026)
- [Zero-OVCD: Bridging Training-Free Foundation Models and Pseudo-Label Learning for Open-Vocabulary Change Detection](https://mlsanity.com/paper/2608.11663/) (2026)
- [QSCP: Beyond Class-Name Prompts for Query-Guided Semantic Change Parsing](https://mlsanity.com/paper/2609.33088/) (2026)

## Abstract (arXiv)

Monitoring Earth's evolving land covers requires methods capable of detecting changes across a wide range of categories and contexts. Existing change detection methods are hindered by their dependency on predefined classes, reducing their effectiveness in open-world applications. To address this issue, we introduce open-vocabulary change detection (OVCD), a novel task that bridges vision and language to detect changes across any category. Considering the lack of high-quality data and annotation, we propose two training-free frameworks, M-C-I and I-M-C, which leverage and integrate off-the-shelf foundation models for the OVCD task. The insight behind the M-C-I framework is to discover all potential changes and then classify these changes, while the insight of I-M-C framework is to identify all targets of interest and then determine whether their states have changed. Based on these two frameworks, we instantiate to obtain several methods, e.g., SAM-DINOv2-SegEarth-OV, Grounding-DINO-SAM2-DINO, etc. Extensive evaluations on 5 benchmark datasets demonstrate the superior generalization and robustness of our OVCD methods over existing supervised and unsupervised methods. To support continued exploration, we release DynamicEarth, a dedicated codebase designed to advance research and application of OVCD. https://likyoo.github.io/DynamicEarth