Hui Qiao4 Tieliang Gong1 Deyu Meng1 Zhi Wang1
Different change detection tasks: (a) Binary change detection aims at discovering all (interested) changes and generating a binary mask; (b) Semantic change detection further identifies the category of changes. However, both can only be trained and evaluated on data with predefined categories; (c) Our proposed OVCD can detect changes in any category according to the user's requirements.
【地表最强(bushi)AI侦探上线！DynamicEarth：让遥感图像图像变化检测秒变"大家来找茬"Pro Max版🌍🔍】
各位看官！还在为传统变化检测模型"死记硬背"有限类别而头秃吗？我们打造的开放词汇变化检测（OVCD）黑科技，让AI秒变"火眼金睛"——无需996式训练，直接调用现成基础模型，就能在卫星图上玩转"大家来找茬"！
👉 两大绝招横扫江湖：
1️⃣ M-C-I框架："先圈地再破案"模式——SAM模型像撒网捕鱼般圈出可疑区域，DINO化身福尔摩斯比对特征，最后CLIP大佬开口定罪名："报告！这里从工地变泳池了！🏗→🏊"
2️⃣ I-M-C框架："指哪打哪"模式——Grounding DINO先锁定目标："给我盯死这片别墅区！" SAM立刻画出精确轮廓，DINO翻出历史档案对比："老板，3号楼偷偷加盖了两层！"
💡 五大杀手锏：
✔️ 开放词汇任你撩：从"查违章建筑"到"找新开体育场"，输入文字指令就能精准定位
✔️ 零训练开箱即用：告别炼丹式调参，现有模型直接"拼积木"
✔️ 抗干扰能力MAX：光照变化？季节更替？我们的AI侦探绝不"疑神疑鬼"
✔️ 跨数据集乱杀：在LEVIR-CD等五大擂台赛吊打传统方法，F1分数飙升30%+
✔️ 代码全家桶奉上：DynamicEarth开源库已就位，就差你来Star⭐️
"DynamicEarth: Where Satellite Sleuthing Meets Open-World Wizardry!" 🌍🕵️♂️
Calling all geo-detectives! Tired of change detection models stuck in "I-Spy-20-Objects" mode? Meet our Open-Vocabulary Change Detection (OVCD) – the Sherlock Holmes of satellite imagery that cracks any visual case you throw at it, zero training required!
🚀 Two Frameworks to Rule Them All:
1️⃣ M-C-I Protocol: "Mask first, ask later!"
- SAM sprays "detective spray" to highlight suspicious zones 🕸️
- DINO plays spot-the-difference with NASA-level precision 🔍
- CLIP drops the mic: "This construction site just morphed into a waterpark!" 🏗️💦
2️⃣ I-M-C Maneuver: "Name it, claim it!"
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Point at a target: "Track every swimming pool in Dubai!" 🏊♂️
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Grounding DINO snaps to attention 👮♂️
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SAM outlines targets like a crime scene investigator 🚧
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DINO cross-examines timelines: "Pool #5 shrank 2 meters – violation alert!" 🚨
💥 Why This Rocks:
✔️ Vocabulary? We Don’t Know Her: Detect "illegal rooftop extensions" or "mysterious crop circles" with equal flair 🌾👽
✔️ No-Training Wheels: Skip endless training marathons – our model’s already bench-pressing foundation models 💪
✔️ Pseudo-Change? GTFO: Seasons change? Shadows shift? Our AI’s got trust issues (in a good way) ☀️❄️
✔️ Dataset Domination: Crushed LEVIR-CD/WHU-CD benchmarks like Godzilla in Tokyo 🏙️💥
✔️ Open-Source Swagger: DynamicEarth codebase – now 100% less "secret sauce"! 👩💻🔓
The two OVCD frameworks proposed in this paper. (a) M-C-I: discover all class-agnostic masks, determine if the mask region has changed, and identify the change class. (b) I-M-C: identify all targets of interest, convert to mask format, and compare if the target has changed.
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.
Our code depends on PyTorch, Detectron, OpenMMLab, SAM ... ...
Please refer to Install Guide for more detailed instruction.
SAM_DINO_SegEarth-OV
python sam_dino_segearth-ov_demo.py --input_image_1 demo_images/A/test_1024.png --input_image_2 demo_images/B/test_1024.png
SAM_DINOv2_SegEarth-OV
python sam_dinov2_segearth-ov_demo.py --input_image_1 demo_images/A/test_1024.png --input_image_2 demo_images/B/test_1024.png
Grounding DINO 1.5-SAM2-DINO
# Get your API token from https://cloud.deepdataspace.com
python gd1.5_sam2_demo.py --gd_api_token [YOUR_TOKEN] --input_image_1 demo_images/A/test_256.png --input_image_2 demo_images/B/test_256.png
APE-DINO
python ape_dino_demo.py --input_image_1 demo_images/A/test_256.png --input_image_2 demo_images/B/test_256.png
APE-DINOv2
python ape_dinov2_demo.py --input_image_1 demo_images/A/test_256.png --input_image_2 demo_images/B/test_256.png
MMGrounding DINO-SAM2-DINO
python mmgd_sam2_dino_demo.py --input_image_1 demo_images/A/test_256.png --input_image_2 demo_images/B/test_256.png
We provide comprehensive evaluation scripts for the LEVIR-CD, WHU-CD, S2Looking, BANDON, SECOND datasets and you can find them in eval.
@article{li2025dynamicearth,
title={DynamicEarth: How Far are We from Open-Vocabulary Change Detection?},
author={Li, Kaiyu and Cao, Xiangyong and Deng, Yupeng and Pang, Chao and Xin, Zepeng and Meng, Deyu and Wang, Zhi},
journal={arXiv preprint arXiv:2501.12931},
year={2025}
}
We sincerely appreciate the following: