# Data-Centric AI for Tropical Agricultural Mapping: Challenges, Strategies and Scalable Solutions

arXiv: https://arxiv.org/abs/2510.16207 · HTML: https://mlsanity.com/paper/2510.16207/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 53 references that are in the map.
- First posted on arXiv 17 Oct 2025.

## Builds on (most cited first, 14 of 14)

- [SpectralGPT: Spectral Remote Sensing Foundation Model](https://mlsanity.com/paper/2311.07113/) (2023)
- [Addressing Failure Prediction by Learning Model Confidence](https://mlsanity.com/paper/1910.04851/) (2019)
- [ST++: Make Self-training Work Better for Semi-supervised Semantic Segmentation](https://mlsanity.com/paper/2106.05095/) (2021)
- [Data-centric AI: Perspectives and Challenges](https://mlsanity.com/paper/2301.04819/) (2023)
- [Predicting into unknown space? Estimating the area of applicability of spatial prediction models](https://mlsanity.com/paper/2005.07939/) (2020)
- [A Sentinel-2 multi-year, multi-country benchmark dataset for crop classification and segmentation with deep learning](https://mlsanity.com/paper/2204.00951/) (2022)
- [Better, Not Just More: Data-Centric Machine Learning for Earth Observation](https://mlsanity.com/paper/2312.05327/) (2023)
- [DIAL: Deep Interactive and Active Learning for Semantic Segmentation in Remote Sensing](https://mlsanity.com/paper/2201.01047/) (2022)
- [Data Excellence for AI: Why Should You Care](https://mlsanity.com/paper/2111.10391/) (2021)
- [Curriculum-style Local-to-global Adaptation for Cross-domain Remote Sensing Image Segmentation](https://mlsanity.com/paper/2203.01539/) (2022)
- [Fine tuning Pre trained Models for Robustness Under Noisy Labels](https://mlsanity.com/paper/2310.17668/) (2023)
- [Data-Centric Digital Agriculture: A Perspective](https://mlsanity.com/paper/2312.03437/) (2023)
- [Learning De-Biased Representations for Remote-Sensing Imagery](https://mlsanity.com/paper/2410.04546/) (2024)
- [Core-Set Selection for Data-efficient Land Cover Segmentation](https://mlsanity.com/paper/2505.01225/) (2025)

## Built on by (most cited first, 1 of 1)

- [AgrI Challenge: A Data-Centric AI Competition for Cross-Team Validation in Agricultural Vision](https://mlsanity.com/paper/2603.07356/) (2026)

## Abstract (arXiv)

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.