Datasets:
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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/projects/LAE/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() |