NickBurns/amazon-sentinel2-forest-segmentation

Amazon Sentinel-2 Forest Segmentation Dataset

Dataset Description

This dataset contains satellite images from the Amazon biome for semantic segmentation of forested areas. The images were extracted from Sentinel-2 Level 2A satellite imagery and converted to GeoTIFF format to preserve all four spectral bands.

Source

• Original Source: Zenodo
• Paper: Bragagnolo, L., da Silva, R.V., & Grzybowski, J.M.V. (2021). Amazon and Atlantic Forest image datasets for semantic segmentation. Zenodo. https://doi.org/10.5281/zenodo.4498086

Dataset Structure

Split	Images	Masks	Description
train	499	499	Training samples with pixel masks
val	100	100	Validation samples with pixel masks
test	20	-	Test samples (no masks provided)
Total	619	599

Image Properties

Property	Value
Format	GeoTIFF (.tif)
Size	512 × 512 pixels
Data type	8-bit unsigned integer (0-255)
Spectral bands	4 (R, G, B, NIR)

Spectral Bands

Band	Sentinel-2 Band	Wavelength	Description
0	B4	664.5 nm	Red
1	B3	559 nm	Green
2	B2	492.4 nm	Blue
3	B8	832.8 nm	Near-Infrared

Label Encoding

Value	Class	Description
0	Background	Non-forested areas (soil, water, urban)
1	Forest	Forested areas

Loading the Dataset

Using HuggingFace Datasets (Recommended)

from datasets import load_dataset

# Load from HuggingFace Hub
dataset = load_dataset("NickBurns/amazon-sentinel2-forest-segmentation")

# Access splits
train_ds = dataset["train"]
val_ds = dataset["val"]
test_ds = dataset["test"]

# Example: access a single sample
sample = train_ds[0]
image = sample["image"]   # shape: (4, 512, 512) - [R, G, B, NIR]
label = sample["label"]   # shape: (512, 512) - binary mask
filename = sample["filename"]

Using Rasterio (Manual Loading)

import rasterio
import numpy as np
from pathlib import Path

def load_sample(image_path, label_path=None):
    """Load a single image and optional mask."""
    with rasterio.open(image_path) as src:
        image = src.read()  # shape: (4, H, W)
    
    label = None
    if label_path and Path(label_path).exists():
        with rasterio.open(label_path) as src:
            label = src.read()  # shape: (H, W)
    
    return image, label

Using torchgeo

from torchgeo.datasets import RasterDataset
from torch.utils.data import DataLoader

# Note: Requires separate handling for multi-band GeoTIFF
class Sentinel2Dataset(RasterDataset):
    filename_glob = "*.tif"
    is_image = True

ds = Sentinel2Dataset("path/to/train/image/")
dl = DataLoader(ds, batch_size=4)

Example Sample

from datasets import load_dataset

ds = load_dataset("NickBurns/amazon-sentinel2-forest-segmentation", split="train")
sample = ds[0]

print(f"Image shape: {sample['image'].shape}")  # (4, 512, 512)
print(f"Label shape: {sample['label'].shape}")  # (512, 512)
print(f"Unique labels: {np.unique(sample['label'])}")  # [0, 1]
print(f"Filename: {sample['filename']}")

Dataset Statistics

Class Distribution (Training Set)

Based on the original Zenodo publication, the dataset was curated to include diverse forest and non-forest coverage for semantic segmentation training.

Geographic Coverage

• Region: Amazon Biome, Brazil
• Satellite: Sentinel-2A
• Acquisition: 2020

License

Creative Commons Attribution 4.0 International (CC-BY 4.0)

Citation

@misc{bragagnolo2021amazon,
  title = {Amazon and Atlantic Forest image datasets for semantic segmentation},
  author = {Bragagnolo, Lucimara and da Silva, Roberto Valmir and Grzybowski, José Mario Vicensi},
  year = {2021},
  publisher = {Zenodo},
  doi = {10.5281/zenodo.4498086},
  url = {https://doi.org/10.5281/zenodo.4498086}
}

Acknowledgments

Original dataset created by researchers at the Federal University of Fronteira Sul, Brazil. Converted and uploaded to HuggingFace for easier access and integration with machine learning workflows.