Mapping Sub-Saharan African Agriculture in High-Resolution Satellite Imagery with Computer Vision & Machine Learning (2017)

You are here

Inside Signal Processing Newsletter Home Page

Top Reasons to Join SPS Today!

1. IEEE Signal Processing Magazine
2. Signal Processing Digital Library*
3. Inside Signal Processing Newsletter
4. SPS Resource Center
5. Career advancement & recognition
6. Discounts on conferences and publications
7. Professional networking
8. Communities for students, young professionals, and women
9. Volunteer opportunities
10. Coming soon! PDH/CEU credits
Click here to learn more.

10 years of news and resources for members of the IEEE Signal Processing Society

Mapping Sub-Saharan African Agriculture in High-Resolution Satellite Imagery with Computer Vision & Machine Learning (2017)

Debats, Stephanie Renee (Princeton University)

Advisor: Caylor, Kelly K.

Smallholder farms dominate in many parts of the world, including Sub-Saharan Africa. These systems are characterized by small, heterogeneous, and often indistinct field patterns, requiring a specialized methodology to map agricultural landcover. In this thesis, they developed a benchmark labeled data set of high-resolution satellite imagery of agricultural fields in South Africa. They presented a new approach to mapping agricultural fields, based on efficient extraction of a vast set of simple, highly correlated, and interdependent features, followed by a random forest classifier. The algorithm achieved similar high performance across agricultural types, including spectrally indistinct smallholder fields, and demonstrated the ability to generalize across large geographic areas. In sensitivity analyses, they determined multi-temporal images provided greater performance gains than the addition of multi-spectral bands.

They also demonstrated how active learning can be incorporated in the algorithm to create smaller, more efficient training data sets, which reduced computational resources, minimized the need for humans to hand-label data, and boosted performance. They designed a patch-based uncertainty metric to drive the active learning framework, based on the regular grid of a crowdsourcing platform, and demonstrated how subject matter experts can be replaced with fleets of crowdsourcing workers. Their active learning algorithm achieved similar performance as an algorithm trained with randomly selected data, but with 62% less data samples.

This thesis furthers the goal of providing accurate agricultural landcover maps, at a scale that is relevant for the dominant smallholder class. Accurate maps are crucial for monitoring and promoting agricultural production. Furthermore, improved agricultural landcover maps will aid a host of other applications, including landcover change assessments, cadastral surveys to strengthen smallholder land rights, and constraints for crop modeling and famine prediction.

Table of Contents:

New Books

SPS on Twitter

  • Share SPS membership with your students, colleagues, and friends! Celebrate - Students and Professionals…
  • DEADLINE APPROACHING: The IEEE Transactions on Multimedia is accepting submissions for a Special Issue on Learning…
  • Celebrate now through 27 February! Get a 50% discount on SPS membership – offers available for Student a…
  • The SPACE Webinar Series continues Tuesday, 23 February at 10:00 AM EST when Dr. Pier Luigi Dragotti presents "Comp…
  • The Brain Space Initiative Talk Series continues on Friday, 26 February at 11:00 AM EST when Dr. Emmanuelle Tognoli…

SPS Videos

Signal Processing in Home Assistants


Multimedia Forensics

Careers in Signal Processing             


Under the Radar