Using Crowdsourcing & Big Data to Understand Agriculture in Sub-Saharan AfricaDee Luo
Mapping Africa Project• Current Issue: Inaccurate and unreliable representation of
agricultural land• Key issues: Food security, predicting expansion
• Solution?• Mapping initiative using Amazon Mechanical Turk• To get data for all of Sub-Saharan Africa, crowdsourcing expensive
Alternatives?• Machine learning; Classification algorithms• Merging fields of remote sensing and computer vision
• Random Forest Algorithm• Schroff, F., Criminisi, A. and Zisserman, A.: Object Class Segmentation
using Random Forests, Proceedings of the British Machine Vision Conference (2008)
Implementation• Feature-based classification : field/nonfield
• RGB• Edge detection• Texture gradients
• Different ways of calculating thresholds• Mean values• Symmetric patches• Absolute points• Channel combinations
• Tokarczyk, P., Wegner J. D., Walk, S., Schindler, K.: Features, Color Spaces, and Boosting: New Insights on Semantic Classification of Remote Sensing Images
Image Hand Labeled Ground Truth
Hand Labeled Ground TruthImage
Additional Work• Acquiring and analysis of LANDSAT data
• Multi-spectral images, combinations of spectral bands• R: filtering by loud cover, growing seasons, etc.
• Hand-digitization of field data set• QGIS: spatial analysis
Current Results/Future Work• Current accuracy at approx. 70%
• About 60% correctly labeled fields• About 80% correctly labeled nonfields
• Stronger accuracy with large fields, much weaker on smaller residential fields
• Future improvements:• Better Imagery – very high resolution (< 1m)• Parameter optimization• More features
Summary
