# Rapid Response Crop Maps in Data Sparse Regions

arXiv: https://arxiv.org/abs/2006.16866 · HTML: https://mlsanity.com/paper/2006.16866/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jun 2020.

## Built on by (most cited first, 14 of 14)

- [CROMA: Remote Sensing Representations with Contrastive Radar-Optical Masked Autoencoders](https://mlsanity.com/paper/2311.00566/) (2023)
- [GEO-Bench: Toward Foundation Models for Earth Monitoring](https://mlsanity.com/paper/2306.03831/) (2023)
- [Lightweight, Pre-trained Transformers for Remote Sensing Timeseries](https://mlsanity.com/paper/2304.14065/) (2023)
- [SustainBench: Benchmarks for Monitoring the Sustainable Development Goals with Machine Learning](https://mlsanity.com/paper/2111.04724/) (2021)
- [Galileo: Learning Global & Local Features of Many Remote Sensing Modalities](https://mlsanity.com/paper/2502.09356/) (2025)
- [Toward Foundation Models for Earth Monitoring: Proposal for a Climate Change Benchmark](https://mlsanity.com/paper/2112.00570/) (2021)
- [TIML: Task-Informed Meta-Learning for Agriculture](https://mlsanity.com/paper/2202.02124/) (2022)
- [Towards Scalable Foundation Model for Multi-modal and Hyperspectral Geospatial Data](https://mlsanity.com/paper/2503.12843/) (2025)
- [Using Machine Learning to generate an open-access cropland map from satellite images time series in the Indian Himalayan Region](https://mlsanity.com/paper/2203.14673/) (2022)
- [HarvestNet: A Dataset for Detecting Smallholder Farming Activity Using Harvest Piles and Remote Sensing](https://mlsanity.com/paper/2308.12061/) (2023)
- [Benchmarking for Practice: Few-Shot Time-Series Crop-Type Classification on the EuroCropsML Dataset](https://mlsanity.com/paper/2504.11022/) (2025)
- [Do Foundation Model Embeddings Improve Cross-Country Crop Yield Generalisation? A Leave-One-Country-Out Evaluation in Sub-Saharan Africa](https://mlsanity.com/paper/2605.08113/) (2026)
- [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)
- [Environment-Aware Satellite Image Generation with Diffusion Models](https://mlsanity.com/paper/2509.24875/) (2025)

## Abstract (arXiv)

Spatial information on cropland distribution, often called cropland or crop maps, are critical inputs for a wide range of agriculture and food security analyses and decisions. However, high-resolution cropland maps are not readily available for most countries, especially in regions dominated by smallholder farming (e.g., sub-Saharan Africa). These maps are especially critical in times of crisis when decision makers need to rapidly design and enact agriculture-related policies and mitigation strategies, including providing humanitarian assistance, dispersing targeted aid, or boosting productivity for farmers. A major challenge for developing crop maps is that many regions do not have readily accessible ground truth data on croplands necessary for training and validating predictive models, and field campaigns are not feasible for collecting labels for rapid response. We present a method for rapid mapping of croplands in regions where little to no ground data is available. We present results for this method in Togo, where we delivered a high-resolution (10 m) cropland map in under 10 days to facilitate rapid response to the COVID-19 pandemic by the Togolese government. This demonstrated a successful transition of machine learning applications research to operational rapid response in a real humanitarian crisis. All maps, data, and code are publicly available to enable future research and operational systems in data-sparse regions.