# How accurate are existing land cover maps for agriculture in Sub-Saharan Africa?

arXiv: https://arxiv.org/abs/2307.02575 · HTML: https://mlsanity.com/paper/2307.02575/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 61 references that are in the map.
- First posted on arXiv 5 Jul 2023.

## Builds on (most cited first, 1 of 1)

- [Evaluation Challenges for Geospatial ML](https://mlsanity.com/paper/2303.18087/) (2023)

## Built on by (most cited first, 11 of 11)

- [Distribution Shifts at Scale: Out-of-distribution Detection in Earth Observation](https://mlsanity.com/paper/2412.13394/) (2024)
- [Contrasting local and global modeling with machine learning and satellite data: A case study estimating tree canopy height in African savannas](https://mlsanity.com/paper/2411.14354/) (2024)
- [Cropland Mapping using Geospatial Embeddings](https://mlsanity.com/paper/2511.02923/) (2025)
- [PRUE: A Practical Recipe for Field Boundary Segmentation at Scale](https://mlsanity.com/paper/2603.27101/) (2026)
- [Embedding -based Crop Type Classification in the Groundnut Basin of Senegal](https://mlsanity.com/paper/2601.16900/) (2026)
- [HarvestNet: A Dataset for Detecting Smallholder Farming Activity Using Harvest Piles and Remote Sensing](https://mlsanity.com/paper/2308.12061/) (2023)
- [Weak Labeling for Cropland Mapping in Africa](https://mlsanity.com/paper/2401.07014/) (2024)
- [Global High Categorical Resolution Land Cover Mapping via Weak Supervision](https://mlsanity.com/paper/2406.00891/) (2024)
- [Evaluating the Role of Training Data Origin for Country-Scale Cropland Mapping in Data-Scarce Regions: A Case Study of Nigeria](https://mlsanity.com/paper/2312.10872/) (2023)
- [Local vs. Global: Local Land-Use and Land-Cover Models Deliver Higher Quality Maps](https://mlsanity.com/paper/2412.00777/) (2024)
- [LC-SLab -- An object-based deep learning framework for large-scale land cover classification from satellite imagery and sparse in-situ labels](https://mlsanity.com/paper/2509.15868/) (2025)

## Abstract (arXiv)

Satellite Earth observations (EO) can provide affordable and timely information for assessing crop conditions and food production. Such monitoring systems are essential in Africa, where there is high food insecurity and sparse agricultural statistics. EO-based monitoring systems require accurate cropland maps to provide information about croplands, but there is a lack of data to determine which of the many available land cover maps most accurately identify cropland in African countries. This study provides a quantitative evaluation and intercomparison of 11 publicly available land cover maps to assess their suitability for cropland classification and EO-based agriculture monitoring in Africa using statistically rigorous reference datasets from 8 countries. We hope the results of this study will help users determine the most suitable map for their needs and encourage future work to focus on resolving inconsistencies between maps and improving accuracy in low-accuracy regions.