| | --- |
| | license: mit |
| | task_categories: |
| | - object-detection |
| | - zero-shot-object-detection |
| | language: |
| | - en |
| | size_categories: |
| | - 1M+ |
| | source_datasets: |
| | - DOTA |
| | - DIOR |
| | - FAIR1M |
| | - NWPU-VHR-10 |
| | - HRSC2016 |
| | - RSOD |
| | - AID |
| | - NWPU-RESISC45 |
| | - SLM |
| | - EMS |
| | tags: |
| | - remote-sensing |
| | - computer-vision |
| | - open-vocabulary |
| | - benchmark |
| | - image-dataset |
| | pretty_name: LAE-1M |
| | --- |
| | |
| |
|
| | # LAE-1M: Locate Anything on Earth Dataset |
| |
|
| | <p align="center"> |
| | <img src="https://jianchengpan.space/LAE-website/assets/LAE-1M.png" alt="LAE-1M" width="600"/> |
| | </p> |
| |
|
| | **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). |
| |
|
| | 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. |
| |
|
| | --- |
| |
|
| | ## Dataset Details |
| |
|
| | ### Dataset Description |
| |
|
| | - **Curated by:** Jiancheng Pan, Yanxing Liu, Yuqian Fu, Muyuan Ma, Jiahao Li, Danda Pani Paudel, Luc Van Gool, Xiaomeng Huang |
| | - **Funded by:** ETH Zürich, INSAIT (partial computing support) |
| | - **Shared by:** LAE-DINO Project Team |
| | - **Language(s):** Not language-specific; visual dataset |
| | - **License:** MIT License |
| |
|
| | ### Dataset Sources |
| |
|
| | - **Repository:** [GitHub - LAE-DINO](https://github.com/jaychempan/LAE-DINO) |
| | - **Paper:** [ArXiv 2408.09110](https://arxiv.org/abs/2408.09110), [AAAI 2025](https://ojs.aaai.org/index.php/AAAI/article/view/32672) |
| | - **Project Page:** [LAE Website](https://jianchengpan.space/LAE-website/index.html) |
| | - **Dataset Download:** [HuggingFace](https://huggingface.co/datasets/jaychempan/LAE-1M) |
| |
|
| | --- |
| |
|
| | ## Dataset Structure |
| |
|
| | | Subset | # Images | # Classes | Format | Description | |
| | |-------------|-------------|-----------|-------------|----------------------------------------------| |
| | | LAE-COD | 400k+ | 20+ | COCO | Coarse-grained detection (AID, EMS, SLM) | |
| | | LAE-FOD | 600k+ | 50+ | COCO | Fine-grained detection (DIOR, DOTAv2, FAIR1M) | |
| | | LAE-80C | 20k (val) | 80 | COCO | Benchmark with 80 semantically distinct classes | |
| |
|
| | All annotations are in **COCO JSON** format with bounding boxes and categories. |
| |
|
| | --- |
| |
|
| | ## Uses |
| |
|
| | ### Direct Use |
| | - Open-Vocabulary Object Detection in Remote Sensing |
| | - Benchmarking zero-shot and few-shot detection models |
| | - Pretraining large vision-language models |
| |
|
| | ### Out-of-Scope Use |
| | - Any tasks requiring personal or sensitive information |
| | - Real-time inference on satellite streams without further optimization |
| |
|
| | --- |
| |
|
| | ## Quick Start |
| |
|
| | ```python |
| | from datasets import load_dataset |
| | |
| | # Load the dataset |
| | dataset = load_dataset("jaychempan/LAE-1M", split="train") |
| | |
| | # Access one example |
| | example = dataset[0] |
| | print(example.keys()) # image, annotations, category_id, etc. |
| | |
| | # Show the image (requires Pillow) |
| | from PIL import Image |
| | import io |
| | |
| | img = Image.open(io.BytesIO(example["image"])) |
| | img.show() |
