8 June 2018 Satellite imagery analysis for automated global food security forecasting
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Abstract
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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Daniela I. Moody, Daniela I. Moody, Steven P. Brumby, Steven P. Brumby, Rick Chartrand, Rick Chartrand, Ryan Keisler, Ryan Keisler, Mark Mathis, Mark Mathis, Carly M. Beneke, Carly M. Beneke, David Nicholaeff, David Nicholaeff, Samuel Skillman, Samuel Skillman, Michael S. Warren, Michael S. Warren, Justin Poehnelt, Justin Poehnelt, } "Satellite imagery analysis for automated global food security forecasting", Proc. SPIE 10644, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXIV, 1064429 (8 June 2018); doi: 10.1117/12.2315960; https://doi.org/10.1117/12.2315960
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