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.
The recent computing performance revolution has driven improvements in sensor, communication, and storage technology. Multi-decadal remote sensing datasets at the petabyte scale are now available in commercial clouds, with new satellite constellations generating petabytes/year of daily high-resolution global coverage imagery. Cloud computing and storage, combined with recent advances in machine learning, are enabling understanding of the world at a scale and at a level of detail never before feasible. We present results from an ongoing effort to develop satellite imagery analysis tools that aggregate temporal, spatial, and spectral information and that can scale with the high-rate and dimensionality of imagery being collected. We focus on the problem of monitoring food crop productivity across the Middle East and North Africa, and show how an analysis-ready, multi-sensor data platform enables quick prototyping of satellite imagery analysis algorithms, from land use/land cover classification and natural resource mapping, to yearly and monthly vegetative health change trends at the structural field level.
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