How accurate are existing land cover maps for agriculture in Sub-Saharan Africa?
Not on the map. Why?
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.
Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 1 of its 61 references that are in the map. First posted on arXiv 5 Jul 2023.
Builds on
1 of 61 references.
References · 1
- 2023Evaluation Challenges for Geospatial MLcited by 8
Built on by
Citing papers per year: 2023: 2; 2024: 5; 2025: 2; 2026: 2.
11 papers.
- 2024Distribution Shifts at Scale: Out-of-distribution Detection in Earth Observationcited by 10
- 2024Contrasting local and global modeling with machine learning and satellite data: A case study estimating tree canopy height in African savannascited by 6
- 2026PRUE: A Practical Recipe for Field Boundary Segmentation at Scalecited by 5
- 2025Cropland Mapping using Geospatial Embeddingscited by 4
- 2026Embedding -based Crop Type Classification in the Groundnut Basin of Senegalcited by 3
- 2023HarvestNet: A Dataset for Detecting Smallholder Farming Activity Using Harvest Piles and Remote Sensingcited by 1
- 2024Weak Labeling for Cropland Mapping in Africacited by 1
- 2024Global High Categorical Resolution Land Cover Mapping via Weak Supervisioncited by 1
- 2023Evaluating the Role of Training Data Origin for Country-Scale Cropland Mapping in Data-Scarce Regions: A Case Study of Nigeriacited by 0
- 2024Local vs. Global: Local Land-Use and Land-Cover Models Deliver Higher Quality Mapscited by 0
- 2025LC-SLab -- An object-based deep learning framework for large-scale land cover classification from satellite imagery and sparse in-situ labelscited by 0
- 2026PRUE: A Practical Recipe for Field Boundary Segmentation at Scale28 Mar 2026
- 2026Embedding -based Crop Type Classification in the Groundnut Basin of Senegal23 Jan 2026
- 2025Cropland Mapping using Geospatial Embeddings4 Nov 2025
- 2025LC-SLab -- An object-based deep learning framework for large-scale land cover classification from satellite imagery and sparse in-situ labels19 Sep 2025
- 2024Distribution Shifts at Scale: Out-of-distribution Detection in Earth Observation18 Dec 2024
- 2024Local vs. Global: Local Land-Use and Land-Cover Models Deliver Higher Quality Maps1 Dec 2024
- 2024Contrasting local and global modeling with machine learning and satellite data: A case study estimating tree canopy height in African savannas21 Nov 2024
- 2024Global High Categorical Resolution Land Cover Mapping via Weak Supervision2 Jun 2024
- 2024Weak Labeling for Cropland Mapping in Africa13 Jan 2024
- 2023Evaluating the Role of Training Data Origin for Country-Scale Cropland Mapping in Data-Scarce Regions: A Case Study of Nigeria18 Dec 2023
- 2023HarvestNet: A Dataset for Detecting Smallholder Farming Activity Using Harvest Piles and Remote Sensing23 Aug 2023