Datasets:
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 0Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
LAE-1M: Locate Anything on Earth Dataset
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
- Repository: GitHub - LAE-DINO
- Paper: ArXiv 2408.09110, AAAI 2025
- Project Page: LAE Website
- Dataset Download: HuggingFace
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()
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