AI-powered automated landscape monitoring at global scale

Mark Hannel; Erin Glennie; Brendan McAndrew; Steven P. Brumby; Amy E. Larson; Mark Mathis; Peter Kerins; Joseph Mazzariello; Megan Hansen; Gracie Ermi

doi:10.1117/12.2683797

4 October 2023 AI-powered automated landscape monitoring at global scale

Mark Hannel, Erin Glennie, Brendan McAndrew, Steven P. Brumby, Amy E. Larson, Mark Mathis, Peter Kerins, Joseph Mazzariello, Megan Hansen, Gracie Ermi

Author Affiliations +

Proceedings Volume 12675, Applications of Machine Learning 2023; 1267507 (2023) https://doi.org/10.1117/12.2683797
Event: SPIE Optical Engineering + Applications, 2023, San Diego, California, United States

Abstract

Recent advances in deep learning, large-scale cloud computing, and open access to decades of Earth observation from public satellite constellations have enabled a breakthrough in automated mapping and monitoring at global scale in near real time. We report on work generating a sequence of annual, global land use and land cover maps at 10 m spatial resolution for years 2017 through 2022, publicly available as an open science product. Each map required processing over 2 million Copernicus Sentinel-2 scenes (approximately 0.6 petabytes). Each map was completed in approximately one week using commercial cloud computing resources. We report our map accuracy and recent work to stabilize the maps across time for monitoring changes across years.

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Citation Download Citation

Mark Hannel, Erin Glennie, Brendan McAndrew, Steven P. Brumby, Amy E. Larson, Mark Mathis, Peter Kerins, Joseph Mazzariello, Megan Hansen, and Gracie Ermi "AI-powered automated landscape monitoring at global scale", Proc. SPIE 12675, Applications of Machine Learning 2023, 1267507 (4 October 2023); https://doi.org/10.1117/12.2683797

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