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  1. .gitattributes +74 -0
  2. LAE-80C/LAE-80C-benchmark.json +3 -0
  3. LAE-80C/LAE-80C-benchmark.txt +1 -0
  4. LAE-80C/LAE-80C-benchmark_categories.json +242 -0
  5. LAE-80C/images.zip +3 -0
  6. LAE-COD/AID/AID.json +3 -0
  7. LAE-COD/AID/images.zip +3 -0
  8. LAE-COD/EMS/EMS.json +3 -0
  9. LAE-COD/EMS/images.zip +3 -0
  10. LAE-COD/NWPU-RESISC45/NWPURESISC45.json +3 -0
  11. LAE-COD/NWPU-RESISC45/images.zip +3 -0
  12. LAE-COD/SLM/SLM.json +0 -0
  13. LAE-COD/SLM/images.zip +3 -0
  14. LAE-FOD/DIOR/DIOR_train.json +3 -0
  15. LAE-FOD/DIOR/DIOR_val.json +0 -0
  16. LAE-FOD/DIOR/processed_DIOR_train.json +3 -0
  17. LAE-FOD/DIOR/processed_LAE-1M_DIOR_train.json +0 -0
  18. LAE-FOD/DIOR/trainval_images.zip +3 -0
  19. LAE-FOD/DOTAv2/DOTAv2_train.json +3 -0
  20. LAE-FOD/DOTAv2/DOTAv2_val.json +0 -0
  21. LAE-FOD/DOTAv2/images.zip +3 -0
  22. LAE-FOD/DOTAv2/processed_DOTAv2_train.json +3 -0
  23. LAE-FOD/DOTAv2/processed_LAE-1M_DOTAv2_train.json +3 -0
  24. LAE-FOD/FAIR1M/images.zip +3 -0
  25. LAE-FOD/FAIR1M/processed_LAE-1M_FAIR1M_train.json +3 -0
  26. LAE-FOD/HRSC2016/images.zip +3 -0
  27. LAE-FOD/HRSC2016/processed_LAE-1M_HRSC2016_train.json +0 -0
  28. LAE-FOD/NWPU VHR-10/images.zip +3 -0
  29. LAE-FOD/NWPU VHR-10/processed_LAE-1M_NWPU-VHR-10_train.json +0 -0
  30. LAE-FOD/Power-Plant/.DS_Store +0 -0
  31. LAE-FOD/Power-Plant/Images.zip +3 -0
  32. LAE-FOD/Power-Plant/processed_LAE-1M_Power-Plant_train.json +0 -0
  33. LAE-FOD/RSOD/images.zip +3 -0
  34. LAE-FOD/RSOD/processed_LAE-1M_RSOD_train.json +0 -0
  35. LAE-FOD/xview/processed_LAE-1M_Xview_train_1024_05.json +3 -0
  36. LAE-FOD/xview/train_images_1024_05.zip +3 -0
  37. README.md +104 -0
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+ ---
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+ license: mit
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+ task_categories:
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+ - object-detection
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+ - zero-shot-object-detection
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+ language:
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+ - en
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+ size_categories:
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+ - 1M+
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+ source_datasets:
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+ - DOTA
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+ - DIOR
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+ - FAIR1M
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+ - NWPU-VHR-10
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+ - HRSC2016
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+ - RSOD
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+ - AID
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+ - NWPU-RESISC45
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+ - SLM
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+ - EMS
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+ tags:
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+ - remote-sensing
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+ - computer-vision
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+ - open-vocabulary
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+ - benchmark
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+ - image-dataset
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+ pretty_name: LAE-1M
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+ ---
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+
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+
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+ # LAE-1M: Locate Anything on Earth Dataset
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+
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+ <p align="center">
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+ <img src="https://jianchengpan.space/projects/LAE/assets/LAE-1M.png" alt="LAE-1M" width="600"/>
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+ </p>
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+
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+ **LAE-1M** (Locate Anything on Earth - 1 Million) is a large-scale **open-vocabulary remote sensing object detection dataset** introduced in the paper *"Locate Anything on Earth: Advancing Open-Vocabulary Object Detection for Remote Sensing Community"* (AAAI 2025).
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+
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+ It contains over **1M images** with **coarse-grained (LAE-COD)** and **fine-grained (LAE-FOD)** annotations, unified in **COCO format**, enabling **zero-shot** and **few-shot** detection in remote sensing.
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+
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+ ---
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+
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+ ## Dataset Details
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+
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+ ### Dataset Description
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+
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+ - **Curated by:** Jiancheng Pan, Yanxing Liu, Yuqian Fu, Muyuan Ma, Jiahao Li, Danda Pani Paudel, Luc Van Gool, Xiaomeng Huang
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+ - **Funded by:** ETH Zürich, INSAIT (partial computing support)
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+ - **Shared by:** LAE-DINO Project Team
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+ - **Language(s):** Not language-specific; visual dataset
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+ - **License:** MIT License
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+
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+ ### Dataset Sources
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+
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+ - **Repository:** [GitHub - LAE-DINO](https://github.com/jaychempan/LAE-DINO)
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+ - **Paper:** [ArXiv 2408.09110](https://arxiv.org/abs/2408.09110), [AAAI 2025](https://ojs.aaai.org/index.php/AAAI/article/view/32672)
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+ - **Project Page:** [LAE Website](https://jianchengpan.space/LAE-website/index.html)
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+ - **Dataset Download:** [HuggingFace](https://huggingface.co/datasets/jaychempan/LAE-1M)
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+
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+ ---
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+
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+ ## Dataset Structure
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+
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+ | Subset | # Images | # Classes | Format | Description |
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+ |-------------|-------------|-----------|-------------|----------------------------------------------|
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+ | LAE-COD | 400k+ | 20+ | COCO | Coarse-grained detection (AID, EMS, SLM) |
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+ | LAE-FOD | 600k+ | 50+ | COCO | Fine-grained detection (DIOR, DOTAv2, FAIR1M) |
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+ | LAE-80C | 20k (val) | 80 | COCO | Benchmark with 80 semantically distinct classes |
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+
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+ All annotations are in **COCO JSON** format with bounding boxes and categories.
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+
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+ ---
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+
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+ ## Uses
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+
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+ ### Direct Use
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+ - Open-Vocabulary Object Detection in Remote Sensing
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+ - Benchmarking zero-shot and few-shot detection models
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+ - Pretraining large vision-language models
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+
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+ ### Out-of-Scope Use
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+ - Any tasks requiring personal or sensitive information
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+ - Real-time inference on satellite streams without further optimization
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+
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+ ---
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+
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+ ## Quick Start
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+
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+ ```python
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+ from datasets import load_dataset
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+
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+ # Load the dataset
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+ dataset = load_dataset("jaychempan/LAE-1M", split="train")
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+
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+ # Access one example
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+ example = dataset[0]
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+ print(example.keys()) # image, annotations, category_id, etc.
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+
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+ # Show the image (requires Pillow)
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+ from PIL import Image
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+ import io
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+
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+ img = Image.open(io.BytesIO(example["image"]))
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+ img.show()