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

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.

⚡ This is an original paraphrased summary — not copied from the abstract. Full paper available at the source link below.

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

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

This summary is based on publicly available metadata and abstract. For the full research paper, visit the original source: