{
  "@context": "https://schema.org",
  "@type": "article",
  "@id": "https://anchorfact.org/kb/ai-land-use-classification",
  "headline": "AI for Land Use Classification: Satellite Image Segmentation, Urban Expansion Mapping, and Agricultural Monitoring",
  "description": "AI classifies every pixel of Earth -- mapping forests, cities, cropland, and water at 10m resolution, updated annually. From ESA's WorldCover global map to Google's near-real-time Dynamic World, deep learning on satellite imagery gives humanity an unprecedented view of how our planet is changing.",
  "dateCreated": "2026-05-24T02:49:13.556Z",
  "dateModified": "2026-05-24",
  "author": {
    "@type": "Organization",
    "name": "AnchorFact"
  },
  "publisher": {
    "@type": "Organization",
    "name": "AnchorFact",
    "url": "https://anchorfact.org"
  },
  "license": "https://creativecommons.org/licenses/by/4.0/",
  "anchorfact:confidence": "high",
  "anchorfact:generationMethod": "ai_assisted",
  "citation": [
    {
      "@type": "CreativeWork",
      "name": "Deep Learning for Land Use and Land Cover Classification from Satellite Imagery: A Comprehensive Survey (2024-2025)",
      "sameAs": "https://arxiv.org/search/?query=land+use+classification+deep+learning+satellite"
    },
    {
      "@type": "CreativeWork",
      "name": "Change Detection in Remote Sensing: Siamese Networks, Transformers, and Foundation Models for Urban Expansion Monitoring",
      "sameAs": "https://arxiv.org/search/?query=change+detection+satellite+urban+expansion"
    }
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}