· 2021
Global land use / land cover with Sentinel 2 and deep learning
- 1,264citations
- 2021year
Short summary
A novel deep learning model trained on over 5 billion human-labeled Sentinel-2 pixels generates a global land use/land cover (LULC) map at 10m resolution with state-of-the-art accuracy.
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Key points
- Developed a deep learning segmentation model for LULC mapping.
- Trained the model on a dataset exceeding 5 billion human-labeled Sentinel-2 pixels.
- Created a global LULC map with 10m resolution.
- Achieved state-of-the-art accuracy in LULC mapping.
- Enables automated LULC mapping from time-series satellite observations.
AI-generated from the title and abstract; the full text is not read.
Abstract
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. 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. Based on a novel, very large dataset of over 5 billion human-labeled Sentinel-2 pixels, we developed and deployed a deep learning segmentation model on Sentinel-2 data to create a global LULC map at 10m resolution that achieves state-of-the-art accuracy and enables automated LULC mapping from time series observations.
The authors' abstract, as published at the source., 2021 · DOI ↗
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Field: Media Technology
Media TechnologyEngineering