Mapping Agricultural Fields in Sub-Saharan Africa with a Computer Vision Approach

Thursday, 18 December 2014: 5:00 PM
Stephanie R Debats1, Dee Luo1, Lyndon D Estes1, Thomas Fuchs2 and Kelly K Caylor1, (1)Princeton University, Princeton, NJ, United States, (2)NASA Jet Propulsion Laboratory, Pasadena, CA, United States
Sub-Saharan Africa is an important focus for food security research, because it is experiencing unprecedented population growth, agricultural activities are largely dominated by smallholder production, and the region is already home to 25% of the world’s undernourished. One of the greatest challenges to monitoring and improving food security in this region is obtaining an accurate accounting of the spatial distribution of agriculture.

Households are the primary units of agricultural production in smallholder communities and typically rely on small fields of less than 2 hectares. Field sizes are directly related to household crop productivity, management choices, and adoption of new technologies. As population and agriculture expand, it becomes increasingly important to understand both the distribution of field sizes as well as how agricultural communities are spatially embedded in the landscape. In addition, household surveys, a common tool for tracking agricultural productivity in Sub-Saharan Africa, would greatly benefit from spatially explicit accounting of fields.

Current gridded land cover data sets do not provide information on individual agricultural fields or the distribution of field sizes. Therefore, we employ cutting edge approaches from the field of computer vision to map fields across Sub-Saharan Africa, including semantic segmentation, discriminative classifiers, and automatic feature selection. Our approach aims to not only improve the binary classification accuracy of cropland, but also to isolate distinct fields, thereby capturing crucial information on size and geometry. Our research focuses on the development of descriptive features across scales to increase the accuracy and geographic range of our computer vision algorithm. Relevant data sets include high-resolution remote sensing imagery and Landsat (30-m) multi-spectral imagery. Training data for field boundaries is derived from hand-digitized data sets as well as crowdsourcing.