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FederCO23/solar-plants-brazil

🛰️ Solar Plants Brazil Solar Plants Brazil is a geospatial dataset for binary semantic segmentation of photovoltaic (PV) solar power stations in satellite imagery. It consists of multi-spectral image tiles (including near-infrared) with pixel-level annotations indicating the presence of solar panels. This dataset enables training and evaluating deep learning models that automatically detect solar farm installations from overhead imagery, supporting applications in renewable… See the full description on the dataset page: https://huggingface.co/datasets/FederCO23/solar-plants-brazil.

sourceHugging Facecc-by-nc-4.0updated 1y agoView on Hugging Face
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🛰️ Solar Plants Brazil

Solar Plants Brazil is a geospatial dataset for binary semantic segmentation of photovoltaic (PV) solar power stations in satellite imagery. It consists of multi-spectral image tiles (including near-infrared) with pixel-level annotations indicating the presence of solar panels. This dataset enables training and evaluating deep learning models that automatically detect solar farm installations from overhead imagery, supporting applications in renewable energy mapping and remote sensing.

Dataset Overview

  • —Purpose: Identify photovoltaic solar plant locations via binary segmentation (panel vs. background) in satellite images
  • —Imagery: 4-band (RGB + Near-Infrared) GeoTIFF tiles at 256×256 pixels each (float32 pixel values)
  • —Labels: Corresponding binary mask for each image (1 = solar panel, 0 = no panel)
  • —Volume: 272 image/mask pairs in total, split into training, validation, and test sets (201 train, 35 val, 36 test)
  • —Geography: Tiles are from various locations across Brazil, focusing on known solar farm sites
  • —Metadata: GeoTIFF files include spatial reference (CRS) and geolocation

📁 Dataset Structure

solar-plants-brazil/
├── train/
│   ├── input/       # 256x256 RGBNIR images (GeoTIFF)
│   └── labels/      # 256x256 binary masks (GeoTIFF)
├── val/
│   ├── input/ 
│   └── labels/
└── test/
    ├── input/
    └── labels/

Each image is named img(<ID>).tif with its corresponding mask target(<ID>).tif. All images are pre-cropped to 256×256 pixels. The masks are binary, with 1 representing solar panels.

🔧 TorchGeo Integration

This dataset is integrated with the TorchGeo library and can be loaded as:

python
from torchgeo.datasets import SolarPlantsBrazil

dataset = SolarPlantsBrazil(split="train", root="data/", download=True)
sample = dataset[0]
image_tensor, mask_tensor = sample["image"], sample["mask"]
print(image_tensor.shape, mask_tensor.shape)

This returns image tensors with 4 channels and single-channel binary masks.

💡 Use Cases

  • —Training segmentation models for solar farm detection
  • —Monitoring solar plant growth over time
  • —Infrastructure planning and spatial analytics
  • —Academic research on geospatial ML

📚 Related Resources

License

This dataset is licensed under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0).

📜 Citation

bibtex
@misc{solarplantsbrazil2024,
  author       = {Federico Bessi},
  title        = {Solar Plants Brazil: A Semantic Segmentation Dataset for Photovoltaic Panel Detection},
  year         = {2024},
  howpublished = {Hugging Face Datasets},
  url          = {https://huggingface.co/datasets/FederCO23/solar-plants-brazil},
  note         = {Preprocessing and training code available at https://github.com/FederCO23/UCSD_MLBootcamp_Capstone}
}

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