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Datasets:
jaychempan
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LAE-1M

Tasks:
Object Detection
Zero-Shot Object Detection
Modalities:
Geospatial
Languages:
English
ArXiv:
Tags:
remote-sensing
computer-vision
open-vocabulary
benchmark
image-dataset
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metadata 
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

LAE-1M

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

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 

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()