Street2Sat: A Machine Learning Pipeline for Generating Ground-truth Geo-referenced Labeled Datasets from Street-Level Images (Proposals Track)

Madhava Paliyam (University of Maryland); Catherine L Nakalembe (University of Maryland); Kevin Liu (University of Maryland); Richard Nyiawung (University of Guelph); Hannah R Kerner (University of Maryland)

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Abstract

Ground-truth labels on crop type and other variables are critically needed to develop machine learning methods that use satellite observations to combat climate change and food insecurity. These labels difficult and costly to obtain over large areas, particularly in Sub-Saharan Africa where they are most scarce. We propose Street2Sat, a new framework for obtaining large data sets of geo-referenced crop type labels obtained from vehicle-mounted cameras that can be extended to other applications. Using preliminary data from Kenya, we present promising results from this approach and identify future to improve the method before operational use in 5 countries.