  A key challenge of supervised learning is the availability of human-labeled
data. We evaluate a big data processing pipeline to auto-generate labels for
remote sensing data. It is based on rasterized statistical features extracted
from surveys such as e.g. LiDAR measurements. Using simple combinations of the
rasterized statistical layers, it is demonstrated that multiple classes can be
generated at accuracies of ~0.9. As proof of concept, we utilize the big
geo-data platform IBM PAIRS to dynamically generate such labels in dense urban
areas with multiple land cover classes. The general method proposed here is
platform independent, and it can be adapted to generate labels for other
satellite modalities in order to enable machine learning on overhead imagery
for land use classification and object detection.
