Data-Centric AI for Tropical Agricultural Mapping: Challenges, Strategies and Scalable Solutions
This work addresses the problem of agricultural mapping in tropical regions for remote sensing and AI practitioners, but it is incremental as it reviews and prioritizes existing data-centric techniques.
The paper tackles the challenges of mapping tropical agriculture via remote sensing, such as scarce high-quality data and high labeling costs, by advocating a data-centric AI pipeline that prioritizes data quality and curation, and proposes a practical pipeline using 9 mature methods for scalable solutions.
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.