A region-wide, multi-year set of crop field boundary labels for Africa
L.D. Estes, A. Wussah, M. Asipunu, M. Gathigi, P. Kova\v{c}i\v{c}, J., Muhando, B.V. Yeboah, F.K. Addai, E.S. Akakpo, M.K. Allotey, P. Amkoya, E., Amponsem, K.D. Donkoh, N. Ha, E. Heltzel, C. Juma, R. Mdawida, A. Miroyo, J., Mucha, J. Mugami, F. Mwawaza, D.A. Nyarko, P. Oduor

TL;DR
This paper presents a large, multi-year dataset of crop field boundary labels across Africa, created using a custom labeling platform and quality assessment procedures, to support machine learning models for crop mapping.
Contribution
The authors developed and released a comprehensive, region-wide crop field boundary dataset for Africa, including quality metrics and uncertainty estimates, to facilitate remote sensing-based agricultural analysis.
Findings
Label quality was moderate for total field extent (0.75).
Small-scale fields in dense croplands are challenging to delineate accurately.
The dataset reveals regional variations in field size and density.
Abstract
African agriculture is undergoing rapid transformation. Annual maps of crop fields are key to understanding the nature of this transformation, but such maps are currently lacking and must be developed using advanced machine learning models trained on high resolution remote sensing imagery. To enable the development of such models, we delineated field boundaries in 33,746 Planet images captured between 2017 and 2023 across the continent using a custom labeling platform with built-in procedures for assessing and mitigating label error. We collected 42,403 labels, including 7,204 labels arising from tasks dedicated to assessing label quality (Class 1 labels), 32,167 from sites mapped once by a single labeller (Class 2) and 3,032 labels from sites where 3 or more labellers were tasked to map the same location (Class 4). Class 1 labels were used to calculate labeller-specific quality scores,…
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Taxonomy
TopicsAgricultural Innovations and Practices · Agricultural risk and resilience · Global trade and economics
