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Global land use / land cover with Sentinel 2 and deep learning

📅 July 11, 2021 👤 Krishna Karra, Caitlin Kontgis, Zoe Statman-Weil et al. 📖 Research Journal 📊 1,242 citations

🤖 Plain-English Summary

Land use/land cover (LULC) maps are foundational geospatial data products needed by analysts and decision makers across governments, civil society, industry, and finance to monitor global environmental change and measure risk to sustainable livelihoods and development. Advances in deep learning and scalable cloud-based compute now provide the analysis capability required to unlock the value in global satellite imagery observations.

🔑 Key Findings

  • There is a strong need for high-level, automated geospatial analysis products that turn these pixels into actionable insights for non-geospatial experts.
  • The Sentinel 2 satellites, first launched in mid-2015, are excellent candidates for LULC mapping due to their high spatial, spectral, and temporal resolution.
  • Advances in deep learning and scalable cloud-based compute now provide the analysis capability required to unlock the value in global satellite imagery observations.

💡 Why This Matters

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

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📋 Article Details

Category 🤖 Artificial Intelligence
Published Jul 11, 2021
Journal Research Journal
Authors Krishna Karra, Caitlin Kontgis, Zoe Statman-Weil, Joseph C. Mazzariello, Mark Mathis
DOI 10.1109/igarss47720.2021.9553499
Citations 1,242
Source OpenAlex

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