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BigEarthNet-43-HMLC

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image_path Continuous urban fabric Discontinuous urban fabric Industrial or commercial units Road and rail networks and associated land Port areas Airports Mineral extraction sites Dump sites Construction sites Green urban areas Sport and leisure facilities Non-irrigated arable land Permanently irrigated land Rice fields Vineyards Fruit trees and berry plantations Olive groves Pastures Annual crops associated with permanent crops Complex cultivation patterns Land principally occupied by agriculture with significant areas of natural vegetation Agro-forestry areas Broad-leaved forest Coniferous forest Mixed forest Natural grassland Moors and heathland Sclerophyllous vegetation Transitional woodland scrub Beaches, dunes, sand plains Bare rock Sparsely vegetated areas Burnt areas Inland marshes Peat bogs Salt marshes Salines Intertidal flats Water courses Water bodies Coastal lagoons Estuaries Sea and ocean Artificial surfaces Urban fabric Industrial, commercial and transport units Mine, dump and construction sites Artificial, nonagricultural vegetated areas Agricultural areas Arable land Permanent crops Pastures Heterogeneous agricultural areas Forests and semi-natural areas Forests Shrub and/or herbaceous vegetation associations Open spaces with little or no vegetation Wetlands Inland wetlands Coastal wetlands Water bodies Continental waters Marine waters
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Extended label set of BigEarthNet-43 for Hierarchical Multi-Label Classification

This dataset contains an extended version of the original label set of BigEarthNet-43 for Hierarchical Multi-Label Classification.

Dataset creation

It was created based on the CORINE Land Cover database of the year 2018 (CLC 2018), which provides detailed information about the land cover classes at multiple levels of the hierarchy

Loading the Dataset

To load the dataset into your project, you can use the following code snippet:

import pandas as pd
from datasets import load_dataset

# Load the dataset from Hugging Face
dataset = load_dataset(
    "marjandl/BigEarthNet-43-HMLC", 
    split='train'
)

data = [line.split('\t')[1:] for line in dataset['text']]
df = pd.DataFrame(data)

# Set the first row as the header and drop it from the DataFrame
df.columns = df.iloc[0]
df = df.drop(0).reset_index(drop=True)

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Size of downloaded dataset files:

97.7 MB

Size of the auto-converted Parquet files:

24.4 MB

Number of rows:

590,327