metadata/metadata.json

{
  "name": "Controlled Diet (CD) Dataset for Methane Plume Detection",
  "version": "1.0.0",
  "description": "A large-scale collection of 4,885 methane (CH₄) plume images captured using optical gas imaging (OGI) technology for semantic segmentation tasks",
  "authors": [
    {
      "name": "Mohamed G. Embaby",
      "affiliation": "Southern Illinois University Carbondale",
      "email": "embaby@siu.edu"
    },
    {
      "name": "Toqi Tahamid Sarker", 
      "affiliation": "Southern Illinois University Carbondale",
      "email": "toqitahamid.sarker@siu.edu"
    },
    {
      "name": "Amer AbuGhazaleh",
      "affiliation": "Southern Illinois University Carbondale",
      "email": "aamer@siu.edu"
    },
    {
      "name": "Khaled R. Ahmed",
      "affiliation": "Southern Illinois University Carbondale", 
      "email": "kahmed@siu.edu"
    }
  ],
  "license": "CC0-1.0",
  "publication": {
    "title": "Optical gas imaging and deep learning for quantifying enteric methane emissions from rumen fermentation in vitro",
    "journal": "IET Image Processing",
    "year": 2025,
    "doi": "10.1049/ipr2.13327",
    "url": "https://doi.org/10.1049/ipr2.13327"
  },
  "funding": {
    "agency": "National Institute of Food and Agriculture, United States Department of Agriculture",
    "award_number": "2022-70001-37404"
  },
  "dataset_info": {
    "total_images": 4885,
    "image_resolution": "640x480",
    "file_format": "PNG",
    "camera": "FLIR GF77 OGI camera",
    "spectral_range": "7-8.5 μm",
    "annotation_type": "semantic segmentation masks",
    "classes": 4,
    "background_method": "ice block thermal contrast"
  },
  "splits": {
    "train": {
      "images": 3905,
      "percentage": 80
    },
    "validation": {
      "images": 496,
      "percentage": 10
    },
    "test": {
      "images": 484,
      "percentage": 10
    }
  },
  "class_distribution": {
    "class_1": {
      "gc_range_ppm": "166-171",
      "diet": "Control (50:50 F:C ratio)",
      "train": 1079,
      "validation": 138,
      "test": 133,
      "total": 1350
    },
    "class_2": {
      "gc_range_ppm": "300-334", 
      "diet": "Low Forage (20:80 F:C ratio)",
      "train": 1268,
      "validation": 162,
      "test": 157,
      "total": 1587
    },
    "class_3": {
      "gc_range_ppm": "457-510",
      "diet": "High Forage (80:20 F:C ratio)", 
      "train": 1558,
      "validation": 196,
      "test": 194,
      "total": 1948
    }
  },
  "experimental_setup": {
    "source": "In vitro continuous culture fermentation system",
    "simulation": "Cow rumen environment",
    "collection_method": "24-hour ANKOM batch culture",
    "validation_methods": ["Gas Chromatography (GC)", "Laser Methane Detector (LMD)"],
    "temperature": "22°C controlled room temperature"
  },
  "mask_generation": {
    "method": "Automated pipeline",
    "steps": [
      "Background subtraction using pre-recorded reference frames",
      "Contrast enhancement for improved plume visibility",
      "Adaptive thresholding for binary separation",
      "Watershed algorithm with Sobel filter elevation maps",
      "Region analysis with size-based filtering",
      "Binary mask generation for pixel-wise annotations"
    ]
  },
  "applications": [
    "Semantic segmentation model training",
    "Agricultural monitoring and assessment",
    "Environmental research on livestock emissions", 
    "Computer vision system development",
    "Climate change mitigation strategy evaluation"
  ],
  "keywords": [
    "optical gas imaging",
    "methane detection", 
    "semantic segmentation",
    "livestock emissions",
    "computer vision",
    "deep learning",
    "agriculture",
    "climate change",
    "FLIR GF77",
    "rumen fermentation"
  ],
  "created": "2025-01-19",
  "updated": "2025-01-19",
  "format_version": "1.0",
  "schema": "https://schema.org/Dataset"
}