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

By Krishna Karra, C. Kontgis, Zoe Statman-Weil, Joseph C. Mazzariello, M. Mathis, S. Brumby. 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, 2021

989 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Ecology, Media Technology

View Paper: https://www.semanticscholar.org/paper/76caafae9c08411d6727c816cbdb987ef3e1d0f1

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. David N. Barton
2. Zander S. Venter

In Ecology

1. Tirthankar Chakraborty
2. T. Simensen
3. G. Singh
4. David N. Barton
5. Zander S. Venter

In Media Technology

1. Yansheng Li

Citations in the corpus

• Global 10 m Land Use Land Cover Datasets: A Comparison of Dynamic World, World Cover and Esri Land Cover (2022, background): “Since 2021, there have been three global Sentinel-based 10 m LULC maps released, including Google’s Dynamic World (DW) [23], ESA’s World Cover 2020 (WC) [24] and Esri’s 2020 Land Cover (Esri) [25].”
• SkySense: A Multi-Modal Remote Sensing Foundation Model Towards Universal Interpretation for Earth Observation Imagery (2023, result): “A comparison with the ESRI LandCover Map [38] Prototype Learning (GCPL).”