AI4SmallFarms

A Dataset for Crop Field Delineation in Southeast Asian Smallholder Farms

Journal Article (2023)
Author(s)

Claudio Persello (University of Twente)

Jeroen van der Grift (University of Twente)

X. Fan (TU Delft - Management Support)

Claudia Paris (University of Twente)

Ronny Hansch (Deutsches Zentrum für Luft- und Raumfahrt (DLR))

mila Koeva (University of Twente)

Andrew Nelson (University of Twente)

Research Group
Management Support
Copyright
© 2023 Claudio Persello, Jeroen Grift, X. Fan, Claudia Paris, Ronny Hansch, Mila Koeva, Andrew Nelson
DOI related publication
https://doi.org/10.1109/LGRS.2023.3323095
More Info
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Publication Year
2023
Language
English
Copyright
© 2023 Claudio Persello, Jeroen Grift, X. Fan, Claudia Paris, Ronny Hansch, Mila Koeva, Andrew Nelson
Research Group
Management Support
Volume number
20
Pages (from-to)
1-5
Reuse Rights

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Abstract

Agricultural field polygons within smallholder farming systems are essential to facilitate the collection of geo-spatial data useful for farmers, managers, and policymakers. However, the limited availability of training labels poses a challenge in developing supervised methods to accurately delineate field boundaries using Earth observation (EO) data. This letter introduces an open dataset for training and benchmarking machine learning methods to delineate agricultural field boundaries in polygon format. The large-scale dataset consists of 439 001 field polygons divided into 62 tiles of approximately 5× 5 km distributed across Vietnam and Cambodia, covering a range of fields and diverse landscape types. The field polygons have been meticulously digitized from satellite images, following a rigorous multistep quality control process and topological consistency checks. Multitemporal composites of Sentinel-2 (S2) images are provided to ensure cloud-free data. We conducted an experimental analysis testing a state-of-the-art deep learning (DL) workflow based on fully convolutional networks (FCNs), contour closing, and polygonization. We anticipate that this large-scale dataset will enable researchers to further enhance the delineation of agricultural fields in smallholder farms and to support the achievement of the Sustainable Development Goals (SDGs). The dataset can be downloaded from https://doi.org/10.17026/dans-xy6-ngg6.