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Datasets:
jaychempan
/
LAE-1M

Tasks:
Object Detection
Zero-Shot Object Detection
Modalities:
Geospatial
Languages:
English
ArXiv:
Tags:
remote-sensing
computer-vision
open-vocabulary
benchmark
image-dataset
License:
Dataset card Files
xet
Community
Dataset Viewer
The dataset viewer is not available for this split.
Cannot extract the features (columns) for the split 'train' of the config 'default' of the dataset.
Error code:   FeaturesError
Exception:    ArrowInvalid
Message:      JSON parse error: Invalid value. in row 0
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/json/json.py", line 174, in _generate_tables
                  df = pandas_read_json(f)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/json/json.py", line 38, in pandas_read_json
                  return pd.read_json(path_or_buf, **kwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/io/json/_json.py", line 791, in read_json
                  json_reader = JsonReader(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/io/json/_json.py", line 905, in __init__
                  self.data = self._preprocess_data(data)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/io/json/_json.py", line 917, in _preprocess_data
                  data = data.read()
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/utils/file_utils.py", line 813, in read_with_retries
                  out = read(*args, **kwargs)
                File "/usr/local/lib/python3.9/codecs.py", line 322, in decode
                  (result, consumed) = self._buffer_decode(data, self.errors, final)
              UnicodeDecodeError: 'utf-8' codec can't decode byte 0xff in position 0: invalid start byte
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 228, in compute_first_rows_from_streaming_response
                  iterable_dataset = iterable_dataset._resolve_features()
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 3422, in _resolve_features
                  features = _infer_features_from_batch(self.with_format(None)._head())
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 2187, in _head
                  return next(iter(self.iter(batch_size=n)))
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 2391, in iter
                  for key, example in iterator:
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1882, in __iter__
                  for key, pa_table in self._iter_arrow():
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1904, in _iter_arrow
                  yield from self.ex_iterable._iter_arrow()
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 499, in _iter_arrow
                  for key, pa_table in iterator:
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 346, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/json/json.py", line 177, in _generate_tables
                  raise e
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/json/json.py", line 151, in _generate_tables
                  pa_table = paj.read_json(
                File "pyarrow/_json.pyx", line 308, in pyarrow._json.read_json
                File "pyarrow/error.pxi", line 154, in pyarrow.lib.pyarrow_internal_check_status
                File "pyarrow/error.pxi", line 91, in pyarrow.lib.check_status
              pyarrow.lib.ArrowInvalid: JSON parse error: Invalid value. in row 0

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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 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()
Downloads last month
120
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

Collection including jaychempan/LAE-1M