Mapping agriculture in tropical areas through remote sensing presents unique challenges, including the lack of high-quality annotated data, the elevated costs of labeling, data variability, and regional generalisation. This paper advocates a Data-Centric Artificial Intelligence (DCAI) perspective and pipeline, emphasizing data quality and curation as key drivers for model robustness and scalability. It reviews and prioritizes techniques such as confident learning, core-set selection, data augmentation, and active learning. The paper highlights the readiness and suitability of 25 distinct strategies in large-scale agricultural mapping pipelines. The tropical context is of high interest, since high cloudiness, diverse crop calendars, and limited datasets limit traditional model-centric approaches. This tutorial outlines practical solutions as a data-centric approach for curating and training AI models better suited to the dynamic realities of tropical agriculture. Finally, we propose a practical pipeline using the 9 most mature and straightforward methods that can be applied to a large-scale tropical agricultural mapping project.
@article{arxiv.2510.16207,
title = {Data-Centric AI for Tropical Agricultural Mapping: Challenges, Strategies and Scalable Solutions},
author = {Mateus Pinto da Silva and Sabrina P. L. P. Correa and Hugo N. Oliveira and Ian M. Nunes and Jefersson A. dos Santos},
journal= {arXiv preprint arXiv:2510.16207},
year = {2025}
}