# Remote Crop Mapping at Scale: Using Satellite Imagery and UAV-Acquired Data as Ground Truth
## 5 Conclusions  
This pilot study indicates the potential to use UAV-acquired imagery rather than traditional ground-observer methods to collect large amounts of accurate ground-truth data, especially for smallholder agricultural systems. When collected via UAVs, ground-truth locations can be georeferenced accurately, which is important in areas where individual plots are small (<1 ha) and intercropping is the predominant practice. Additionally, imagery can be reviewed to verify ground cover and extract different features such as the density of vegetation coverage. More examples can easily be added to the training and testing datasets at any time based on the intent of the model. Limitations to collecting ground-truth examples in this manner include restrictions on site selection in terms of the elevation and slope at which UAVs can fly and regulatory constraints.  
The UAV-based ground-truth dataset was used for training and testing an ML model for seven selected landcover categories, including four key crops in Rwanda. The overall accuracy of the model was 83%, with accuracies of 91% and 84% for maize and beans, respectively. Cassava was classified with lower accuracy (67%), and the model tended to confuse it with the catch-all class of other vegetation. Model accuracy may be improved by creating mixed land use categories to better capture intercropping and bananas growing in residential areas.  
Future work will benefit from further increasing the size of the ground-truth datasets (note: our sample size included 1251 total examples that were divided between training and testing datasets) and adding categories to the limited taxonomy we worked with. The addition of these categories will need to be accompanied by an increase in the number of data points assuming 100 samples per class and ideally per AEZ as a typical rule of thumb for the minimum size. Further attention needs to be given to creating accurate and up-to-date shapefiles for landcover categories that do not require the resolution and timeliness needed for agricultural areas. The availability of these shapefiles will allow the downstream models to focus on agricultural classes and improve the overall classification accuracy.  
Data curation, J.P., M.O., and N.U.; formal analysis, M.H.-C, J.P., and D.S.T.; investigation, J.R., R.H.B., D.L., and D.S.T.; methodology, N.U., J.R., R.H.B., D.L., and D.S.T.; software, M.H.-C., J.P., and M.O.; project administration and supervision, J.R., R.H.B., D.L., and D.S.T. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
We gratefully acknowledge the financial support of RTI International through its Grand Challenge Initiative. We have benefited greatly from discussions with stakeholders in the Government of Rwanda, United States Agency for International Development, United Kingdom Department for International Development, European Union, World Bank, UN Food and Agriculture Organization, and many non-government organizations operating in Rwanda. [PERSON] from Vanguard Economics also provided important support to advance this analysis. We thank Charis Unmanned Aerial Solutions for executing UAV flights.  
The authors declare no conflicts of interest.