用数据为中心方法解决热带农业遥感标注难问题
Data-Centric AI for Tropical Agricultural Mapping: Challenges, Strategies and Scalable Solutions
- 聚焦数据质量，采用精选策略提升模型鲁棒性
- 提出9种成熟方法组成的可扩展映射流程
- 适合应对云层多、作物周期复杂的真实场景
通过遥感技术绘制热带地区农业地图面临独特挑战：高质量标注数据匮乏、标注成本高、数据差异大及区域泛化困难。本文倡导数据为中心的人工智能（DCAI）视角与流程，强调数据质量与整理是提升模型稳健性和可扩展性的关键。综述并优先排序了置信学习、核心集选择、数据增强和主动学习等技术。论文指出25种不同策略在大规模农业制图流程中的成熟度与适用性。热带环境因高云量、作物周期多样及数据有限，难以适用传统模型为中心的方法。本文提供实用解决方案，构建更贴合热带农业动态现实的数据驱动训练流程。最后，提出一个由9个最成熟且易实施的方法构成的实用管道，适用于大规模热带农业制图项目。
原文摘要 · Abstract (English)
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.
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