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import json
import datasets
class SSL4EOEUForest(datasets.GeneratorBasedBuilder):
"""
Metadata generator for the SSL4EO-EU-Forest dataset, cf. https://huggingface.co/datasets/dm4eo/ssl4eo_eu_forest .
"""
def _info(self):
"""
Provides details on metadata structure, citation, and credits.
"""
return datasets.DatasetInfo(
description="SSL4EO-EU Forest dataset metadata",
features=datasets.Features({
# data sample ID
"group_id": datasets.Value("string"),
# relative path (without HuggingFace URL) of forest mask
"mask_path": datasets.Value("string"),
# got bounding box in lat-lon coords
"bbox_epsg4326": datasets.Sequence(datasets.Value("float32")),
# image dimensions in width and height
"mask_width": datasets.Value("int32"),
"mask_height": datasets.Value("int32"),
# do the above dimensions match for all the images?
"dimensions_match": datasets.Value("bool"),
# 12-band Sentinel-2 L2A cloud-free images for all seasons in bounding box
"images": datasets.Sequence({
# relative path (without HuggingFace URL) of Sentinel-2 imagery
"path": datasets.Value("string"),
# start time for data recording
"timestamp_start": datasets.Value("string"),
# end time for data recording
"timestamp_end": datasets.Value("string"),
# Sentinel-2 tile ID
"tile_id": datasets.Value("string"),
# season in northern hemisphere
"season": datasets.Value("string"),
# image dimensions
"width": datasets.Value("int32"),
"height": datasets.Value("int32")
})
}),
# which keys refer to (input, output) data for supervised
supervised_keys=("images", "mask_path"),
# BibTeX on how to cite this work
citation="""@misc{ssl4eo_eu_forest,
author = {Nassim Ait Ali Braham and Conrad M Albrecht},
title = {SSL4EO-EU Forest Dataset},
year = {2025},
howpublished = {https://huggingface.co/datasets/dm4eo/ssl4eo-eu-forest},
note = {This work was carried under the EvoLand project, cf. https://www.evo-land.eu . This project has received funding from the European Union's Horizon Europe research and innovation programme under grant agreement No. 101082130.}
}""",
# project homepage
homepage="https://www.evo-land.eu",
# data license
license="CC-BY-4.0",
)
def _split_generators(self, dl_manager):
files = dl_manager.iter_files(["meta.jsonl"])
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={"files": files},
)
]
def _generate_examples(self, files):
for file_url in files:
# print for path resolution verification (may appear multiple times with num_proc > 1)
print("Streaming from:", file_url)
# In streaming mode open(file_url) is supported by datasets' patched I/O
with open(file_url, encoding="utf-8") as f:
for idx, line in enumerate(f):
data = json.loads(line)
yield idx, data
def features_to_croissant(features):
"""
Convert a HF dataset feature into a Croissant-compatible description.
"""
def convert_feature(name:str, feature:datasets.features.features.Features):
if isinstance(feature, datasets.Value):
return {
"name": name,
"dataType": feature.dtype,
"description": f"{name} field"
}
elif isinstance(feature, datasets.Sequence):
inner = feature.feature
if isinstance(inner, dict): # nested structure
return {
"name": name,
"isArray": True,
"description": f"{name} sequence",
"features": [convert_feature(k, v) for k, v in inner.items()]
}
elif isinstance(inner, Value): # flat sequence
return {
"name": name,
"isArray": True,
"description": f"{name} sequence",
"dataType": inner.dtype
}
else:
return {
"name": name,
"dataType": "unknown",
"description": f"{name} field"
}
return [convert_feature(name, feature) for name, feature in features.items()]
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