Print Email Facebook Twitter AI4SmallFarms Title AI4SmallFarms: A Dataset for Crop Field Delineation in Southeast Asian Smallholder Farms Author Persello, Claudio (University of Twente) Grift, Jeroen (University of Twente) Fan, X. (TU Delft Management Support) Paris, Claudia (University of Twente) Hansch, Ronny (Deutsches Zentrum für Luft- und Raumfahrt e.V. (DLR)) Koeva, Mila (University of Twente) Nelson, Andrew (University of Twente) Date 2023 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. Subject Cambodiacrop field boundariesdeep learning (DL)Sentinel-2 (S2) datasmallholder farmsVietnam To reference this document use: http://resolver.tudelft.nl/uuid:1f1a4724-360f-45b0-8184-b244e644b728 DOI https://doi.org/10.1109/LGRS.2023.3323095 ISSN 1545-598X Source IEEE Geoscience and Remote Sensing Letters, 20, 1-5 Part of collection Institutional Repository Document type journal article Rights © 2023 Claudio Persello, Jeroen Grift, X. Fan, Claudia Paris, Ronny Hansch, Mila Koeva, Andrew Nelson Files PDF IET_Intelligent_Trans_Sys ... _model.pdf 1.96 MB Close viewer /islandora/object/uuid:1f1a4724-360f-45b0-8184-b244e644b728/datastream/OBJ/view